Overview

Brought to you by YData

Dataset statistics

Number of variables124
Number of observations1700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory992.1 B

Variable types

Numeric2
Text6
Categorical116

Alerts

ANT_CA_S_n is highly overall correlated with ASP_S_n and 6 other fieldsHigh correlation
ASP_S_n is highly overall correlated with ANT_CA_S_n and 5 other fieldsHigh correlation
B_BLOK_S_n is highly overall correlated with ANT_CA_S_n and 6 other fieldsHigh correlation
D_AD_KBRIG is highly overall correlated with S_AD_KBRIGHigh correlation
D_AD_ORIT is highly overall correlated with K_SH_POST and 1 other fieldsHigh correlation
FIB_G_POST is highly overall correlated with GT_POST and 4 other fieldsHigh correlation
FK_STENOK is highly overall correlated with IBS_POST and 1 other fieldsHigh correlation
GB is highly overall correlated with SIM_GIPERTHigh correlation
GEPAR_S_n is highly overall correlated with ANT_CA_S_n and 5 other fieldsHigh correlation
GIPER_NA is highly overall correlated with GIPO_K and 1 other fieldsHigh correlation
GIPO_K is highly overall correlated with GIPER_NA and 1 other fieldsHigh correlation
GT_POST is highly overall correlated with FIB_G_POST and 4 other fieldsHigh correlation
IBS_POST is highly overall correlated with FK_STENOK and 1 other fieldsHigh correlation
ID is highly overall correlated with LET_ISHigh correlation
K_SH_POST is highly overall correlated with D_AD_ORIT and 6 other fieldsHigh correlation
LET_IS is highly overall correlated with ID and 1 other fieldsHigh correlation
LID_KB is highly overall correlated with NA_KB and 1 other fieldsHigh correlation
LID_S_n is highly overall correlated with ANT_CA_S_n and 5 other fieldsHigh correlation
MP_TP_POST is highly overall correlated with FIB_G_POST and 5 other fieldsHigh correlation
NA_BLOOD is highly overall correlated with GIPER_NA and 1 other fieldsHigh correlation
NA_KB is highly overall correlated with LID_KB and 1 other fieldsHigh correlation
NA_R_1_n is highly overall correlated with LID_S_nHigh correlation
NA_R_2_n is highly overall correlated with NA_R_3_n and 4 other fieldsHigh correlation
NA_R_3_n is highly overall correlated with NA_R_2_n and 4 other fieldsHigh correlation
NITR_S is highly overall correlated with ANT_CA_S_n and 6 other fieldsHigh correlation
NOT_NA_2_n is highly overall correlated with NA_R_2_n and 3 other fieldsHigh correlation
NOT_NA_3_n is highly overall correlated with NA_R_2_n and 3 other fieldsHigh correlation
NOT_NA_KB is highly overall correlated with LID_KB and 1 other fieldsHigh correlation
O_L_POST is highly overall correlated with FIB_G_POST and 4 other fieldsHigh correlation
RAZRIV is highly overall correlated with LET_ISHigh correlation
R_AB_2_n is highly overall correlated with NA_R_2_n and 3 other fieldsHigh correlation
R_AB_3_n is highly overall correlated with NA_R_2_n and 3 other fieldsHigh correlation
SIM_GIPERT is highly overall correlated with GBHigh correlation
STENOK_AN is highly overall correlated with FK_STENOK and 1 other fieldsHigh correlation
SVT_POST is highly overall correlated with FIB_G_POST and 4 other fieldsHigh correlation
S_AD_KBRIG is highly overall correlated with D_AD_KBRIGHigh correlation
S_AD_ORIT is highly overall correlated with D_AD_ORIT and 1 other fieldsHigh correlation
TIKL_S_n is highly overall correlated with ANT_CA_S_n and 6 other fieldsHigh correlation
TRENT_S_n is highly overall correlated with ANT_CA_S_n and 6 other fieldsHigh correlation
ant_im is highly overall correlated with inf_im and 1 other fieldsHigh correlation
endocr_01 is highly overall correlated with endocr_02 and 1 other fieldsHigh correlation
endocr_02 is highly overall correlated with endocr_01 and 1 other fieldsHigh correlation
endocr_03 is highly overall correlated with endocr_01 and 1 other fieldsHigh correlation
fibr_ter_01 is highly overall correlated with fibr_ter_02 and 5 other fieldsHigh correlation
fibr_ter_02 is highly overall correlated with fibr_ter_01 and 5 other fieldsHigh correlation
fibr_ter_03 is highly overall correlated with fibr_ter_01 and 5 other fieldsHigh correlation
fibr_ter_05 is highly overall correlated with fibr_ter_01 and 5 other fieldsHigh correlation
fibr_ter_06 is highly overall correlated with fibr_ter_01 and 5 other fieldsHigh correlation
fibr_ter_07 is highly overall correlated with fibr_ter_01 and 5 other fieldsHigh correlation
fibr_ter_08 is highly overall correlated with fibr_ter_01 and 5 other fieldsHigh correlation
inf_im is highly overall correlated with ant_imHigh correlation
lat_im is highly overall correlated with ant_imHigh correlation
n_p_ecg_p_01 is highly overall correlated with n_p_ecg_p_03 and 24 other fieldsHigh correlation
n_p_ecg_p_03 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_04 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_05 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_06 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_07 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_08 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_09 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_10 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_11 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_p_ecg_p_12 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_01 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_02 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_03 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_04 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_05 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_06 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_08 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_09 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
n_r_ecg_p_10 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
np_01 is highly overall correlated with np_04 and 12 other fieldsHigh correlation
np_04 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
np_05 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
np_07 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
np_08 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
np_09 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
np_10 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
nr_01 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
nr_02 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
nr_03 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
nr_04 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
nr_07 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
nr_08 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
nr_11 is highly overall correlated with np_01 and 12 other fieldsHigh correlation
ritm_ecg_p_01 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
ritm_ecg_p_02 is highly overall correlated with MP_TP_POST and 25 other fieldsHigh correlation
ritm_ecg_p_04 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
ritm_ecg_p_06 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
ritm_ecg_p_07 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
ritm_ecg_p_08 is highly overall correlated with n_p_ecg_p_01 and 24 other fieldsHigh correlation
zab_leg_01 is highly overall correlated with zab_leg_02 and 3 other fieldsHigh correlation
zab_leg_02 is highly overall correlated with zab_leg_01 and 3 other fieldsHigh correlation
zab_leg_03 is highly overall correlated with zab_leg_01 and 3 other fieldsHigh correlation
zab_leg_04 is highly overall correlated with zab_leg_01 and 3 other fieldsHigh correlation
zab_leg_06 is highly overall correlated with zab_leg_01 and 3 other fieldsHigh correlation
IBS_NASL is highly imbalanced (81.5%) Imbalance
SIM_GIPERT is highly imbalanced (83.9%) Imbalance
ZSN_A is highly imbalanced (67.0%) Imbalance
nr_11 is highly imbalanced (83.4%) Imbalance
nr_01 is highly imbalanced (92.4%) Imbalance
nr_02 is highly imbalanced (88.4%) Imbalance
nr_03 is highly imbalanced (84.8%) Imbalance
nr_04 is highly imbalanced (86.1%) Imbalance
nr_07 is highly imbalanced (93.5%) Imbalance
nr_08 is highly imbalanced (92.4%) Imbalance
np_01 is highly imbalanced (93.8%) Imbalance
np_04 is highly imbalanced (93.5%) Imbalance
np_05 is highly imbalanced (91.1%) Imbalance
np_07 is highly imbalanced (94.2%) Imbalance
np_08 is highly imbalanced (92.5%) Imbalance
np_09 is highly imbalanced (93.8%) Imbalance
np_10 is highly imbalanced (93.5%) Imbalance
endocr_01 is highly imbalanced (60.7%) Imbalance
endocr_02 is highly imbalanced (86.2%) Imbalance
endocr_03 is highly imbalanced (92.6%) Imbalance
zab_leg_01 is highly imbalanced (72.5%) Imbalance
zab_leg_02 is highly imbalanced (74.2%) Imbalance
zab_leg_03 is highly imbalanced (88.0%) Imbalance
zab_leg_04 is highly imbalanced (94.6%) Imbalance
zab_leg_06 is highly imbalanced (91.3%) Imbalance
S_AD_KBRIG is highly imbalanced (51.2%) Imbalance
D_AD_KBRIG is highly imbalanced (54.8%) Imbalance
O_L_POST is highly imbalanced (74.4%) Imbalance
K_SH_POST is highly imbalanced (84.1%) Imbalance
MP_TP_POST is highly imbalanced (73.3%) Imbalance
SVT_POST is highly imbalanced (93.5%) Imbalance
GT_POST is highly imbalanced (93.5%) Imbalance
FIB_G_POST is highly imbalanced (91.6%) Imbalance
post_im is highly imbalanced (57.9%) Imbalance
IM_PG_P is highly imbalanced (87.5%) Imbalance
ritm_ecg_p_02 is highly imbalanced (53.5%) Imbalance
ritm_ecg_p_04 is highly imbalanced (66.2%) Imbalance
ritm_ecg_p_06 is highly imbalanced (72.1%) Imbalance
ritm_ecg_p_08 is highly imbalanced (61.5%) Imbalance
n_r_ecg_p_01 is highly imbalanced (64.1%) Imbalance
n_r_ecg_p_02 is highly imbalanced (74.8%) Imbalance
n_r_ecg_p_04 is highly imbalanced (62.3%) Imbalance
n_r_ecg_p_05 is highly imbalanced (62.1%) Imbalance
n_r_ecg_p_06 is highly imbalanced (69.1%) Imbalance
n_r_ecg_p_08 is highly imbalanced (76.0%) Imbalance
n_r_ecg_p_09 is highly imbalanced (76.6%) Imbalance
n_r_ecg_p_10 is highly imbalanced (76.6%) Imbalance
n_p_ecg_p_01 is highly imbalanced (76.6%) Imbalance
n_p_ecg_p_03 is highly imbalanced (69.1%) Imbalance
n_p_ecg_p_04 is highly imbalanced (75.7%) Imbalance
n_p_ecg_p_05 is highly imbalanced (76.6%) Imbalance
n_p_ecg_p_06 is highly imbalanced (70.1%) Imbalance
n_p_ecg_p_07 is highly imbalanced (57.2%) Imbalance
n_p_ecg_p_08 is highly imbalanced (75.1%) Imbalance
n_p_ecg_p_09 is highly imbalanced (74.2%) Imbalance
n_p_ecg_p_10 is highly imbalanced (68.7%) Imbalance
n_p_ecg_p_11 is highly imbalanced (69.9%) Imbalance
n_p_ecg_p_12 is highly imbalanced (60.8%) Imbalance
fibr_ter_01 is highly imbalanced (92.6%) Imbalance
fibr_ter_02 is highly imbalanced (91.9%) Imbalance
fibr_ter_03 is highly imbalanced (81.5%) Imbalance
fibr_ter_05 is highly imbalanced (95.2%) Imbalance
fibr_ter_06 is highly imbalanced (93.7%) Imbalance
fibr_ter_07 is highly imbalanced (94.6%) Imbalance
fibr_ter_08 is highly imbalanced (95.9%) Imbalance
KFK_BLOOD is highly imbalanced (98.8%) Imbalance
R_AB_1_n is highly imbalanced (52.3%) Imbalance
R_AB_2_n is highly imbalanced (61.1%) Imbalance
R_AB_3_n is highly imbalanced (67.6%) Imbalance
NITR_S is highly imbalanced (64.6%) Imbalance
NA_R_2_n is highly imbalanced (67.3%) Imbalance
NA_R_3_n is highly imbalanced (65.8%) Imbalance
NOT_NA_1_n is highly imbalanced (56.9%) Imbalance
NOT_NA_2_n is highly imbalanced (64.7%) Imbalance
NOT_NA_3_n is highly imbalanced (62.5%) Imbalance
B_BLOK_S_n is highly imbalanced (62.0%) Imbalance
TIKL_S_n is highly imbalanced (87.1%) Imbalance
FIBR_PREDS is highly imbalanced (53.1%) Imbalance
PREDS_TAH is highly imbalanced (90.8%) Imbalance
JELUD_TAH is highly imbalanced (83.3%) Imbalance
FIBR_JELUD is highly imbalanced (75.0%) Imbalance
A_V_BLOK is highly imbalanced (78.8%) Imbalance
OTEK_LANC is highly imbalanced (55.2%) Imbalance
RAZRIV is highly imbalanced (79.7%) Imbalance
DRESSLER is highly imbalanced (73.9%) Imbalance
REC_IM is highly imbalanced (55.2%) Imbalance
P_IM_STEN is highly imbalanced (57.3%) Imbalance
ID is uniformly distributed Uniform
ID has unique values Unique
LET_IS has 1429 (84.1%) zeros Zeros

Reproduction

Analysis started2024-11-19 17:23:55.392592
Analysis finished2024-11-19 17:24:58.171295
Duration1 minute and 2.78 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850.5
Minimum1
Maximum1700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2024-11-19T17:24:58.504764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile85.95
Q1425.75
median850.5
Q31275.25
95-th percentile1615.05
Maximum1700
Range1699
Interquartile range (IQR)849.5

Descriptive statistics

Standard deviation490.89205
Coefficient of variation (CV)0.57718054
Kurtosis-1.2
Mean850.5
Median Absolute Deviation (MAD)425
Skewness0
Sum1445850
Variance240975
MonotonicityStrictly increasing
2024-11-19T17:24:58.864856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1700 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
1661 1
 
0.1%
1662 1
 
0.1%
1663 1
 
0.1%
1664 1
 
0.1%
1665 1
 
0.1%
1666 1
 
0.1%
Other values (1690) 1690
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1700 1
0.1%
1699 1
0.1%
1698 1
0.1%
1697 1
0.1%
1696 1
0.1%
1695 1
0.1%
1694 1
0.1%
1693 1
0.1%
1692 1
0.1%
1691 1
0.1%

AGE
Text

Distinct63
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2024-11-19T17:24:59.221222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9952941
Min length1

Characters and Unicode

Total characters3392
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row77
2nd row55
3rd row52
4th row68
5th row60
ValueCountFrequency (%)
63 90
 
5.3%
65 81
 
4.8%
62 79
 
4.6%
64 68
 
4.0%
70 66
 
3.9%
52 57
 
3.4%
61 54
 
3.2%
66 54
 
3.2%
55 53
 
3.1%
57 53
 
3.1%
Other values (53) 1045
61.5%
2024-11-19T17:24:59.855220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 755
22.3%
5 632
18.6%
7 514
15.2%
4 344
10.1%
3 260
 
7.7%
2 210
 
6.2%
8 208
 
6.1%
0 186
 
5.5%
1 142
 
4.2%
9 133
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 755
22.3%
5 632
18.6%
7 514
15.2%
4 344
10.1%
3 260
 
7.7%
2 210
 
6.2%
8 208
 
6.1%
0 186
 
5.5%
1 142
 
4.2%
9 133
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 755
22.3%
5 632
18.6%
7 514
15.2%
4 344
10.1%
3 260
 
7.7%
2 210
 
6.2%
8 208
 
6.1%
0 186
 
5.5%
1 142
 
4.2%
9 133
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 755
22.3%
5 632
18.6%
7 514
15.2%
4 344
10.1%
3 260
 
7.7%
2 210
 
6.2%
8 208
 
6.1%
0 186
 
5.5%
1 142
 
4.2%
9 133
 
3.9%

SEX
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
1065 
0
635 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1065
62.6%
0 635
37.4%

Length

2024-11-19T17:25:00.145183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:00.400294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1065
62.6%
0 635
37.4%

Most occurring characters

ValueCountFrequency (%)
1 1065
62.6%
0 635
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1065
62.6%
0 635
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1065
62.6%
0 635
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1065
62.6%
0 635
37.4%

INF_ANAM
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1060 
1
410 
2
147 
3
 
79
?
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1060
62.4%
1 410
 
24.1%
2 147
 
8.6%
3 79
 
4.6%
? 4
 
0.2%

Length

2024-11-19T17:25:00.628902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:00.858261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1060
62.4%
1 410
 
24.1%
2 147
 
8.6%
3 79
 
4.6%
4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1060
62.4%
1 410
 
24.1%
2 147
 
8.6%
3 79
 
4.6%
? 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1060
62.4%
1 410
 
24.1%
2 147
 
8.6%
3 79
 
4.6%
? 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1060
62.4%
1 410
 
24.1%
2 147
 
8.6%
3 79
 
4.6%
? 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1060
62.4%
1 410
 
24.1%
2 147
 
8.6%
3 79
 
4.6%
? 4
 
0.2%

STENOK_AN
Categorical

High correlation 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
661 
6
332 
1
146 
2
137 
5
125 
Other values (3)
299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 661
38.9%
6 332
19.5%
1 146
 
8.6%
2 137
 
8.1%
5 125
 
7.4%
3 117
 
6.9%
? 106
 
6.2%
4 76
 
4.5%

Length

2024-11-19T17:25:01.104380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:01.353385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 661
38.9%
6 332
19.5%
1 146
 
8.6%
2 137
 
8.1%
5 125
 
7.4%
3 117
 
6.9%
106
 
6.2%
4 76
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 661
38.9%
6 332
19.5%
1 146
 
8.6%
2 137
 
8.1%
5 125
 
7.4%
3 117
 
6.9%
? 106
 
6.2%
4 76
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 661
38.9%
6 332
19.5%
1 146
 
8.6%
2 137
 
8.1%
5 125
 
7.4%
3 117
 
6.9%
? 106
 
6.2%
4 76
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 661
38.9%
6 332
19.5%
1 146
 
8.6%
2 137
 
8.1%
5 125
 
7.4%
3 117
 
6.9%
? 106
 
6.2%
4 76
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 661
38.9%
6 332
19.5%
1 146
 
8.6%
2 137
 
8.1%
5 125
 
7.4%
3 117
 
6.9%
? 106
 
6.2%
4 76
 
4.5%

FK_STENOK
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2
854 
0
661 
?
 
73
3
 
54
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 854
50.2%
0 661
38.9%
? 73
 
4.3%
3 54
 
3.2%
1 47
 
2.8%
4 11
 
0.6%

Length

2024-11-19T17:25:01.634109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:01.867799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 854
50.2%
0 661
38.9%
73
 
4.3%
3 54
 
3.2%
1 47
 
2.8%
4 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 854
50.2%
0 661
38.9%
? 73
 
4.3%
3 54
 
3.2%
1 47
 
2.8%
4 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 854
50.2%
0 661
38.9%
? 73
 
4.3%
3 54
 
3.2%
1 47
 
2.8%
4 11
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 854
50.2%
0 661
38.9%
? 73
 
4.3%
3 54
 
3.2%
1 47
 
2.8%
4 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 854
50.2%
0 661
38.9%
? 73
 
4.3%
3 54
 
3.2%
1 47
 
2.8%
4 11
 
0.6%

IBS_POST
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2
683 
1
548 
0
418 
?
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 683
40.2%
1 548
32.2%
0 418
24.6%
? 51
 
3.0%

Length

2024-11-19T17:25:02.130830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:02.355209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 683
40.2%
1 548
32.2%
0 418
24.6%
51
 
3.0%

Most occurring characters

ValueCountFrequency (%)
2 683
40.2%
1 548
32.2%
0 418
24.6%
? 51
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 683
40.2%
1 548
32.2%
0 418
24.6%
? 51
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 683
40.2%
1 548
32.2%
0 418
24.6%
? 51
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 683
40.2%
1 548
32.2%
0 418
24.6%
? 51
 
3.0%

IBS_NASL
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
1628 
0
 
45
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row0
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 1628
95.8%
0 45
 
2.6%
1 27
 
1.6%

Length

2024-11-19T17:25:02.613310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:02.829815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1628
95.8%
0 45
 
2.6%
1 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
? 1628
95.8%
0 45
 
2.6%
1 27
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 1628
95.8%
0 45
 
2.6%
1 27
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 1628
95.8%
0 45
 
2.6%
1 27
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 1628
95.8%
0 45
 
2.6%
1 27
 
1.6%

GB
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2
880 
0
605 
3
195 
1
 
11
?
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 880
51.8%
0 605
35.6%
3 195
 
11.5%
1 11
 
0.6%
? 9
 
0.5%

Length

2024-11-19T17:25:03.061374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:03.301204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 880
51.8%
0 605
35.6%
3 195
 
11.5%
1 11
 
0.6%
9
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 880
51.8%
0 605
35.6%
3 195
 
11.5%
1 11
 
0.6%
? 9
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 880
51.8%
0 605
35.6%
3 195
 
11.5%
1 11
 
0.6%
? 9
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 880
51.8%
0 605
35.6%
3 195
 
11.5%
1 11
 
0.6%
? 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 880
51.8%
0 605
35.6%
3 195
 
11.5%
1 11
 
0.6%
? 9
 
0.5%

SIM_GIPERT
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1635 
1
 
57
?
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1635
96.2%
1 57
 
3.4%
? 8
 
0.5%

Length

2024-11-19T17:25:03.575561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:03.789970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1635
96.2%
1 57
 
3.4%
8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1635
96.2%
1 57
 
3.4%
? 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1635
96.2%
1 57
 
3.4%
? 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1635
96.2%
1 57
 
3.4%
? 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1635
96.2%
1 57
 
3.4%
? 8
 
0.5%

DLIT_AG
Categorical

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
551 
7
432 
?
248 
6
165 
1
93 
Other values (4)
211 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row0
3rd row2
4th row3
5th row7

Common Values

ValueCountFrequency (%)
0 551
32.4%
7 432
25.4%
? 248
14.6%
6 165
 
9.7%
1 93
 
5.5%
5 73
 
4.3%
2 58
 
3.4%
3 58
 
3.4%
4 22
 
1.3%

Length

2024-11-19T17:25:04.028794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:04.498319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 551
32.4%
7 432
25.4%
248
14.6%
6 165
 
9.7%
1 93
 
5.5%
5 73
 
4.3%
2 58
 
3.4%
3 58
 
3.4%
4 22
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 551
32.4%
7 432
25.4%
? 248
14.6%
6 165
 
9.7%
1 93
 
5.5%
5 73
 
4.3%
2 58
 
3.4%
3 58
 
3.4%
4 22
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 551
32.4%
7 432
25.4%
? 248
14.6%
6 165
 
9.7%
1 93
 
5.5%
5 73
 
4.3%
2 58
 
3.4%
3 58
 
3.4%
4 22
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 551
32.4%
7 432
25.4%
? 248
14.6%
6 165
 
9.7%
1 93
 
5.5%
5 73
 
4.3%
2 58
 
3.4%
3 58
 
3.4%
4 22
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 551
32.4%
7 432
25.4%
? 248
14.6%
6 165
 
9.7%
1 93
 
5.5%
5 73
 
4.3%
2 58
 
3.4%
3 58
 
3.4%
4 22
 
1.3%

ZSN_A
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1468 
1
 
103
?
 
54
3
 
29
2
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1468
86.4%
1 103
 
6.1%
? 54
 
3.2%
3 29
 
1.7%
2 27
 
1.6%
4 19
 
1.1%

Length

2024-11-19T17:25:04.806272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:05.045494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1468
86.4%
1 103
 
6.1%
54
 
3.2%
3 29
 
1.7%
2 27
 
1.6%
4 19
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1468
86.4%
1 103
 
6.1%
? 54
 
3.2%
3 29
 
1.7%
2 27
 
1.6%
4 19
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1468
86.4%
1 103
 
6.1%
? 54
 
3.2%
3 29
 
1.7%
2 27
 
1.6%
4 19
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1468
86.4%
1 103
 
6.1%
? 54
 
3.2%
3 29
 
1.7%
2 27
 
1.6%
4 19
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1468
86.4%
1 103
 
6.1%
? 54
 
3.2%
3 29
 
1.7%
2 27
 
1.6%
4 19
 
1.1%

nr_11
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1637 
1
 
42
?
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1637
96.3%
1 42
 
2.5%
? 21
 
1.2%

Length

2024-11-19T17:25:05.308748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:05.530173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1637
96.3%
1 42
 
2.5%
21
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1637
96.3%
1 42
 
2.5%
? 21
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1637
96.3%
1 42
 
2.5%
? 21
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1637
96.3%
1 42
 
2.5%
? 21
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1637
96.3%
1 42
 
2.5%
? 21
 
1.2%

nr_01
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1675 
?
 
21
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Length

2024-11-19T17:25:05.838083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:06.057477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1675
98.5%
21
 
1.2%
1 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

nr_02
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1660 
?
 
21
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1660
97.6%
? 21
 
1.2%
1 19
 
1.1%

Length

2024-11-19T17:25:06.286591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:06.510110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1660
97.6%
21
 
1.2%
1 19
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1660
97.6%
? 21
 
1.2%
1 19
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1660
97.6%
? 21
 
1.2%
1 19
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1660
97.6%
? 21
 
1.2%
1 19
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1660
97.6%
? 21
 
1.2%
1 19
 
1.1%

nr_03
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1644 
1
 
35
?
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1644
96.7%
1 35
 
2.1%
? 21
 
1.2%

Length

2024-11-19T17:25:06.770983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:06.988936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1644
96.7%
1 35
 
2.1%
21
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1644
96.7%
1 35
 
2.1%
? 21
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1644
96.7%
1 35
 
2.1%
? 21
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1644
96.7%
1 35
 
2.1%
? 21
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1644
96.7%
1 35
 
2.1%
? 21
 
1.2%

nr_04
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1650 
1
 
29
?
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1650
97.1%
1 29
 
1.7%
? 21
 
1.2%

Length

2024-11-19T17:25:07.221437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:07.442036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1650
97.1%
1 29
 
1.7%
21
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1650
97.1%
1 29
 
1.7%
? 21
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1650
97.1%
1 29
 
1.7%
? 21
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1650
97.1%
1 29
 
1.7%
? 21
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1650
97.1%
1 29
 
1.7%
? 21
 
1.2%

nr_07
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1678 
?
 
21
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1678
98.7%
? 21
 
1.2%
1 1
 
0.1%

Length

2024-11-19T17:25:07.697027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:07.910660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1678
98.7%
21
 
1.2%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1678
98.7%
? 21
 
1.2%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1678
98.7%
? 21
 
1.2%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1678
98.7%
? 21
 
1.2%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1678
98.7%
? 21
 
1.2%
1 1
 
0.1%

nr_08
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1675 
?
 
21
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Length

2024-11-19T17:25:08.140327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:08.364611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1675
98.5%
21
 
1.2%
1 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1675
98.5%
? 21
 
1.2%
1 4
 
0.2%

np_01
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1680 
?
 
18
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Length

2024-11-19T17:25:08.609570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:09.457747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1680
98.8%
18
 
1.1%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

np_04
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1679 
?
 
18
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Length

2024-11-19T17:25:10.030713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:10.699126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1679
98.8%
18
 
1.1%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

np_05
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1671 
?
 
18
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1671
98.3%
? 18
 
1.1%
1 11
 
0.6%

Length

2024-11-19T17:25:10.993455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:11.418334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1671
98.3%
18
 
1.1%
1 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1671
98.3%
? 18
 
1.1%
1 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1671
98.3%
? 18
 
1.1%
1 11
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1671
98.3%
? 18
 
1.1%
1 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1671
98.3%
? 18
 
1.1%
1 11
 
0.6%

np_07
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1681 
?
 
18
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1681
98.9%
? 18
 
1.1%
1 1
 
0.1%

Length

2024-11-19T17:25:11.712154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:12.149329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1681
98.9%
18
 
1.1%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1681
98.9%
? 18
 
1.1%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1681
98.9%
? 18
 
1.1%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1681
98.9%
? 18
 
1.1%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1681
98.9%
? 18
 
1.1%
1 1
 
0.1%

np_08
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1676 
?
 
18
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1676
98.6%
? 18
 
1.1%
1 6
 
0.4%

Length

2024-11-19T17:25:12.511993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:12.954702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1676
98.6%
18
 
1.1%
1 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1676
98.6%
? 18
 
1.1%
1 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1676
98.6%
? 18
 
1.1%
1 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1676
98.6%
? 18
 
1.1%
1 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1676
98.6%
? 18
 
1.1%
1 6
 
0.4%

np_09
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1680 
?
 
18
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Length

2024-11-19T17:25:13.471116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:13.820531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1680
98.8%
18
 
1.1%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 18
 
1.1%
1 2
 
0.1%

np_10
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1679 
?
 
18
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Length

2024-11-19T17:25:14.070492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:14.296022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1679
98.8%
18
 
1.1%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1679
98.8%
? 18
 
1.1%
1 3
 
0.2%

endocr_01
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1461 
1
228 
?
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1461
85.9%
1 228
 
13.4%
? 11
 
0.6%

Length

2024-11-19T17:25:14.527035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:14.744942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1461
85.9%
1 228
 
13.4%
11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1461
85.9%
1 228
 
13.4%
? 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1461
85.9%
1 228
 
13.4%
? 11
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1461
85.9%
1 228
 
13.4%
? 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1461
85.9%
1 228
 
13.4%
? 11
 
0.6%

endocr_02
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1648 
1
 
42
?
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1648
96.9%
1 42
 
2.5%
? 10
 
0.6%

Length

2024-11-19T17:25:14.978484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:15.207284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1648
96.9%
1 42
 
2.5%
10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1648
96.9%
1 42
 
2.5%
? 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1648
96.9%
1 42
 
2.5%
? 10
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1648
96.9%
1 42
 
2.5%
? 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1648
96.9%
1 42
 
2.5%
? 10
 
0.6%

endocr_03
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1677 
1
 
13
?
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Length

2024-11-19T17:25:15.707786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:15.926750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

zab_leg_01
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1559 
1
 
134
?
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1559
91.7%
1 134
 
7.9%
? 7
 
0.4%

Length

2024-11-19T17:25:16.170855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:16.384704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1559
91.7%
1 134
 
7.9%
7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1559
91.7%
1 134
 
7.9%
? 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1559
91.7%
1 134
 
7.9%
? 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1559
91.7%
1 134
 
7.9%
? 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1559
91.7%
1 134
 
7.9%
? 7
 
0.4%

zab_leg_02
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1572 
1
 
121
?
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1572
92.5%
1 121
 
7.1%
? 7
 
0.4%

Length

2024-11-19T17:25:16.606407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:16.810890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1572
92.5%
1 121
 
7.1%
7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1572
92.5%
1 121
 
7.1%
? 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1572
92.5%
1 121
 
7.1%
? 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1572
92.5%
1 121
 
7.1%
? 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1572
92.5%
1 121
 
7.1%
? 7
 
0.4%

zab_leg_03
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1656 
1
 
37
?
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1656
97.4%
1 37
 
2.2%
? 7
 
0.4%

Length

2024-11-19T17:25:17.025875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:17.245957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1656
97.4%
1 37
 
2.2%
7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1656
97.4%
1 37
 
2.2%
? 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1656
97.4%
1 37
 
2.2%
? 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1656
97.4%
1 37
 
2.2%
? 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1656
97.4%
1 37
 
2.2%
? 7
 
0.4%

zab_leg_04
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1684 
1
 
9
?
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1684
99.1%
1 9
 
0.5%
? 7
 
0.4%

Length

2024-11-19T17:25:17.464119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:17.670834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1684
99.1%
1 9
 
0.5%
7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1684
99.1%
1 9
 
0.5%
? 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1684
99.1%
1 9
 
0.5%
? 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1684
99.1%
1 9
 
0.5%
? 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1684
99.1%
1 9
 
0.5%
? 7
 
0.4%

zab_leg_06
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1671 
1
 
22
?
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1671
98.3%
1 22
 
1.3%
? 7
 
0.4%

Length

2024-11-19T17:25:17.890325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:18.103874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1671
98.3%
1 22
 
1.3%
7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1671
98.3%
1 22
 
1.3%
? 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1671
98.3%
1 22
 
1.3%
? 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1671
98.3%
1 22
 
1.3%
? 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1671
98.3%
1 22
 
1.3%
? 7
 
0.4%

S_AD_KBRIG
Categorical

High correlation  Imbalance 

Distinct31
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
1076 
140
 
91
130
 
77
160
 
73
120
 
65
Other values (26)
318 

Length

Max length3
Median length1
Mean length1.6929412
Min length1

Characters and Unicode

Total characters2878
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row?
2nd row?
3rd row150
4th row?
5th row190

Common Values

ValueCountFrequency (%)
? 1076
63.3%
140 91
 
5.4%
130 77
 
4.5%
160 73
 
4.3%
120 65
 
3.8%
110 46
 
2.7%
150 44
 
2.6%
170 30
 
1.8%
180 29
 
1.7%
100 26
 
1.5%
Other values (21) 143
 
8.4%

Length

2024-11-19T17:25:18.376970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1076
63.3%
140 91
 
5.4%
130 77
 
4.5%
160 73
 
4.3%
120 65
 
3.8%
110 46
 
2.7%
150 44
 
2.6%
170 30
 
1.8%
180 29
 
1.7%
100 26
 
1.5%
Other values (21) 143
 
8.4%

Most occurring characters

ValueCountFrequency (%)
? 1076
37.4%
0 639
22.2%
1 582
20.2%
2 114
 
4.0%
4 102
 
3.5%
6 83
 
2.9%
3 79
 
2.7%
5 76
 
2.6%
8 54
 
1.9%
9 37
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 1076
37.4%
0 639
22.2%
1 582
20.2%
2 114
 
4.0%
4 102
 
3.5%
6 83
 
2.9%
3 79
 
2.7%
5 76
 
2.6%
8 54
 
1.9%
9 37
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 1076
37.4%
0 639
22.2%
1 582
20.2%
2 114
 
4.0%
4 102
 
3.5%
6 83
 
2.9%
3 79
 
2.7%
5 76
 
2.6%
8 54
 
1.9%
9 37
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 1076
37.4%
0 639
22.2%
1 582
20.2%
2 114
 
4.0%
4 102
 
3.5%
6 83
 
2.9%
3 79
 
2.7%
5 76
 
2.6%
8 54
 
1.9%
9 37
 
1.3%

D_AD_KBRIG
Categorical

High correlation  Imbalance 

Distinct22
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
1076 
80
167 
90
154 
100
 
86
70
 
78
Other values (17)
139 

Length

Max length3
Median length1
Mean length1.4335294
Min length1

Characters and Unicode

Total characters2437
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.4%

Sample

1st row?
2nd row?
3rd row100
4th row?
5th row100

Common Values

ValueCountFrequency (%)
? 1076
63.3%
80 167
 
9.8%
90 154
 
9.1%
100 86
 
5.1%
70 78
 
4.6%
60 59
 
3.5%
120 16
 
0.9%
110 15
 
0.9%
40 11
 
0.6%
0 7
 
0.4%
Other values (12) 31
 
1.8%

Length

2024-11-19T17:25:18.663679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1076
63.3%
80 167
 
9.8%
90 154
 
9.1%
100 86
 
5.1%
70 78
 
4.6%
60 59
 
3.5%
120 16
 
0.9%
110 15
 
0.9%
40 11
 
0.6%
0 7
 
0.4%
Other values (12) 31
 
1.8%

Most occurring characters

ValueCountFrequency (%)
? 1076
44.2%
0 698
28.6%
8 173
 
7.1%
9 156
 
6.4%
1 136
 
5.6%
7 80
 
3.3%
6 62
 
2.5%
2 20
 
0.8%
5 18
 
0.7%
4 13
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 1076
44.2%
0 698
28.6%
8 173
 
7.1%
9 156
 
6.4%
1 136
 
5.6%
7 80
 
3.3%
6 62
 
2.5%
2 20
 
0.8%
5 18
 
0.7%
4 13
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 1076
44.2%
0 698
28.6%
8 173
 
7.1%
9 156
 
6.4%
1 136
 
5.6%
7 80
 
3.3%
6 62
 
2.5%
2 20
 
0.8%
5 18
 
0.7%
4 13
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 1076
44.2%
0 698
28.6%
8 173
 
7.1%
9 156
 
6.4%
1 136
 
5.6%
7 80
 
3.3%
6 62
 
2.5%
2 20
 
0.8%
5 18
 
0.7%
4 13
 
0.5%

S_AD_ORIT
Categorical

High correlation 

Distinct33
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
267 
130
242 
120
220 
140
218 
160
154 
Other values (28)
599 

Length

Max length3
Median length3
Mean length2.6176471
Min length1

Characters and Unicode

Total characters4450
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row180
2nd row120
3rd row180
4th row120
5th row160

Common Values

ValueCountFrequency (%)
? 267
15.7%
130 242
14.2%
120 220
12.9%
140 218
12.8%
160 154
9.1%
110 123
7.2%
150 96
 
5.6%
180 68
 
4.0%
100 55
 
3.2%
170 54
 
3.2%
Other values (23) 203
11.9%

Length

2024-11-19T17:25:18.950488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
267
15.7%
130 242
14.2%
120 220
12.9%
140 218
12.8%
160 154
9.1%
110 123
7.2%
150 96
 
5.6%
180 68
 
4.0%
100 55
 
3.2%
170 54
 
3.2%
Other values (23) 203
11.9%

Most occurring characters

ValueCountFrequency (%)
0 1495
33.6%
1 1414
31.8%
2 280
 
6.3%
? 267
 
6.0%
3 247
 
5.6%
4 227
 
5.1%
6 176
 
4.0%
5 125
 
2.8%
8 92
 
2.1%
7 66
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1495
33.6%
1 1414
31.8%
2 280
 
6.3%
? 267
 
6.0%
3 247
 
5.6%
4 227
 
5.1%
6 176
 
4.0%
5 125
 
2.8%
8 92
 
2.1%
7 66
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1495
33.6%
1 1414
31.8%
2 280
 
6.3%
? 267
 
6.0%
3 247
 
5.6%
4 227
 
5.1%
6 176
 
4.0%
5 125
 
2.8%
8 92
 
2.1%
7 66
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1495
33.6%
1 1414
31.8%
2 280
 
6.3%
? 267
 
6.0%
3 247
 
5.6%
4 227
 
5.1%
6 176
 
4.0%
5 125
 
2.8%
8 92
 
2.1%
7 66
 
1.5%

D_AD_ORIT
Categorical

High correlation 

Distinct21
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
80
502 
90
302 
?
267 
100
201 
70
166 
Other values (16)
262 

Length

Max length3
Median length2
Mean length2.0076471
Min length1

Characters and Unicode

Total characters3413
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row100
2nd row90
3rd row100
4th row70
5th row90

Common Values

ValueCountFrequency (%)
80 502
29.5%
90 302
17.8%
? 267
15.7%
100 201
11.8%
70 166
 
9.8%
60 84
 
4.9%
110 59
 
3.5%
40 28
 
1.6%
120 23
 
1.4%
0 18
 
1.1%
Other values (11) 50
 
2.9%

Length

2024-11-19T17:25:19.240604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80 502
29.5%
90 302
17.8%
267
15.7%
100 201
11.8%
70 166
 
9.8%
60 84
 
4.9%
110 59
 
3.5%
40 28
 
1.6%
120 23
 
1.4%
0 18
 
1.1%
Other values (11) 50
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 1620
47.5%
8 506
 
14.8%
1 357
 
10.5%
9 309
 
9.1%
? 267
 
7.8%
7 169
 
5.0%
6 85
 
2.5%
4 32
 
0.9%
5 32
 
0.9%
2 27
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1620
47.5%
8 506
 
14.8%
1 357
 
10.5%
9 309
 
9.1%
? 267
 
7.8%
7 169
 
5.0%
6 85
 
2.5%
4 32
 
0.9%
5 32
 
0.9%
2 27
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1620
47.5%
8 506
 
14.8%
1 357
 
10.5%
9 309
 
9.1%
? 267
 
7.8%
7 169
 
5.0%
6 85
 
2.5%
4 32
 
0.9%
5 32
 
0.9%
2 27
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1620
47.5%
8 506
 
14.8%
1 357
 
10.5%
9 309
 
9.1%
? 267
 
7.8%
7 169
 
5.0%
6 85
 
2.5%
4 32
 
0.9%
5 32
 
0.9%
2 27
 
0.8%

O_L_POST
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1578 
1
 
110
?
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1578
92.8%
1 110
 
6.5%
? 12
 
0.7%

Length

2024-11-19T17:25:19.529699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:19.748406image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1578
92.8%
1 110
 
6.5%
12
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1578
92.8%
1 110
 
6.5%
? 12
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1578
92.8%
1 110
 
6.5%
? 12
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1578
92.8%
1 110
 
6.5%
? 12
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1578
92.8%
1 110
 
6.5%
? 12
 
0.7%

K_SH_POST
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1639 
1
 
46
?
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1639
96.4%
1 46
 
2.7%
? 15
 
0.9%

Length

2024-11-19T17:25:19.982474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:20.193839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1639
96.4%
1 46
 
2.7%
15
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1639
96.4%
1 46
 
2.7%
? 15
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1639
96.4%
1 46
 
2.7%
? 15
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1639
96.4%
1 46
 
2.7%
? 15
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1639
96.4%
1 46
 
2.7%
? 15
 
0.9%

MP_TP_POST
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1572 
1
 
114
?
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1572
92.5%
1 114
 
6.7%
? 14
 
0.8%

Length

2024-11-19T17:25:20.442811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:20.655559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1572
92.5%
1 114
 
6.7%
14
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1572
92.5%
1 114
 
6.7%
? 14
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1572
92.5%
1 114
 
6.7%
? 14
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1572
92.5%
1 114
 
6.7%
? 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1572
92.5%
1 114
 
6.7%
? 14
 
0.8%

SVT_POST
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1680 
?
 
12
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Length

2024-11-19T17:25:20.877281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:21.082912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1680
98.8%
12
 
0.7%
1 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

GT_POST
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1680 
?
 
12
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Length

2024-11-19T17:25:21.308299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:21.538485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1680
98.8%
12
 
0.7%
1 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
? 12
 
0.7%
1 8
 
0.5%

FIB_G_POST
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1673 
1
 
15
?
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1673
98.4%
1 15
 
0.9%
? 12
 
0.7%

Length

2024-11-19T17:25:21.770601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:21.987228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1673
98.4%
1 15
 
0.9%
12
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1673
98.4%
1 15
 
0.9%
? 12
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1673
98.4%
1 15
 
0.9%
? 12
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1673
98.4%
1 15
 
0.9%
? 12
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1673
98.4%
1 15
 
0.9%
? 12
 
0.7%

ant_im
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
660 
4
492 
1
392 
?
83 
2
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row4
4th row0
5th row4

Common Values

ValueCountFrequency (%)
0 660
38.8%
4 492
28.9%
1 392
23.1%
? 83
 
4.9%
2 39
 
2.3%
3 34
 
2.0%

Length

2024-11-19T17:25:22.234296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:22.490210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 660
38.8%
4 492
28.9%
1 392
23.1%
83
 
4.9%
2 39
 
2.3%
3 34
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 660
38.8%
4 492
28.9%
1 392
23.1%
? 83
 
4.9%
2 39
 
2.3%
3 34
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 660
38.8%
4 492
28.9%
1 392
23.1%
? 83
 
4.9%
2 39
 
2.3%
3 34
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 660
38.8%
4 492
28.9%
1 392
23.1%
? 83
 
4.9%
2 39
 
2.3%
3 34
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 660
38.8%
4 492
28.9%
1 392
23.1%
? 83
 
4.9%
2 39
 
2.3%
3 34
 
2.0%

lat_im
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
838 
0
576 
2
97 
?
 
80
3
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 838
49.3%
0 576
33.9%
2 97
 
5.7%
? 80
 
4.7%
3 72
 
4.2%
4 37
 
2.2%

Length

2024-11-19T17:25:22.751722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:22.994171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 838
49.3%
0 576
33.9%
2 97
 
5.7%
80
 
4.7%
3 72
 
4.2%
4 37
 
2.2%

Most occurring characters

ValueCountFrequency (%)
1 838
49.3%
0 576
33.9%
2 97
 
5.7%
? 80
 
4.7%
3 72
 
4.2%
4 37
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 838
49.3%
0 576
33.9%
2 97
 
5.7%
? 80
 
4.7%
3 72
 
4.2%
4 37
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 838
49.3%
0 576
33.9%
2 97
 
5.7%
? 80
 
4.7%
3 72
 
4.2%
4 37
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 838
49.3%
0 576
33.9%
2 97
 
5.7%
? 80
 
4.7%
3 72
 
4.2%
4 37
 
2.2%

inf_im
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
937 
1
195 
2
191 
4
176 
3
121 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 937
55.1%
1 195
 
11.5%
2 191
 
11.2%
4 176
 
10.4%
3 121
 
7.1%
? 80
 
4.7%

Length

2024-11-19T17:25:23.261121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:23.530251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 937
55.1%
1 195
 
11.5%
2 191
 
11.2%
4 176
 
10.4%
3 121
 
7.1%
80
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 937
55.1%
1 195
 
11.5%
2 191
 
11.2%
4 176
 
10.4%
3 121
 
7.1%
? 80
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 937
55.1%
1 195
 
11.5%
2 191
 
11.2%
4 176
 
10.4%
3 121
 
7.1%
? 80
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 937
55.1%
1 195
 
11.5%
2 191
 
11.2%
4 176
 
10.4%
3 121
 
7.1%
? 80
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 937
55.1%
1 195
 
11.5%
2 191
 
11.2%
4 176
 
10.4%
3 121
 
7.1%
? 80
 
4.7%

post_im
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1370 
1
157 
?
 
72
2
 
52
3
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1370
80.6%
1 157
 
9.2%
? 72
 
4.2%
2 52
 
3.1%
3 35
 
2.1%
4 14
 
0.8%

Length

2024-11-19T17:25:23.833617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:24.255111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1370
80.6%
1 157
 
9.2%
72
 
4.2%
2 52
 
3.1%
3 35
 
2.1%
4 14
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1370
80.6%
1 157
 
9.2%
? 72
 
4.2%
2 52
 
3.1%
3 35
 
2.1%
4 14
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1370
80.6%
1 157
 
9.2%
? 72
 
4.2%
2 52
 
3.1%
3 35
 
2.1%
4 14
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1370
80.6%
1 157
 
9.2%
? 72
 
4.2%
2 52
 
3.1%
3 35
 
2.1%
4 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1370
80.6%
1 157
 
9.2%
? 72
 
4.2%
2 52
 
3.1%
3 35
 
2.1%
4 14
 
0.8%

IM_PG_P
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1649 
1
 
50
?
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1649
97.0%
1 50
 
2.9%
? 1
 
0.1%

Length

2024-11-19T17:25:24.763852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:25.173833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1649
97.0%
1 50
 
2.9%
1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1649
97.0%
1 50
 
2.9%
? 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1649
97.0%
1 50
 
2.9%
? 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1649
97.0%
1 50
 
2.9%
? 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1649
97.0%
1 50
 
2.9%
? 1
 
0.1%

ritm_ecg_p_01
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
1029 
0
519 
?
152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1029
60.5%
0 519
30.5%
? 152
 
8.9%

Length

2024-11-19T17:25:25.564901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:25.947933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1029
60.5%
0 519
30.5%
152
 
8.9%

Most occurring characters

ValueCountFrequency (%)
1 1029
60.5%
0 519
30.5%
? 152
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1029
60.5%
0 519
30.5%
? 152
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1029
60.5%
0 519
30.5%
? 152
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1029
60.5%
0 519
30.5%
? 152
 
8.9%

ritm_ecg_p_02
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1453 
?
152 
1
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1453
85.5%
? 152
 
8.9%
1 95
 
5.6%

Length

2024-11-19T17:25:26.440155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:26.856105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1453
85.5%
152
 
8.9%
1 95
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 1453
85.5%
? 152
 
8.9%
1 95
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1453
85.5%
? 152
 
8.9%
1 95
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1453
85.5%
? 152
 
8.9%
1 95
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1453
85.5%
? 152
 
8.9%
1 95
 
5.6%

ritm_ecg_p_04
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1525 
?
 
152
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1525
89.7%
? 152
 
8.9%
1 23
 
1.4%

Length

2024-11-19T17:25:27.165688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:27.904960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1525
89.7%
152
 
8.9%
1 23
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1525
89.7%
? 152
 
8.9%
1 23
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1525
89.7%
? 152
 
8.9%
1 23
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1525
89.7%
? 152
 
8.9%
1 23
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1525
89.7%
? 152
 
8.9%
1 23
 
1.4%

ritm_ecg_p_06
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1547 
?
 
152
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1547
91.0%
? 152
 
8.9%
1 1
 
0.1%

Length

2024-11-19T17:25:28.354295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:28.691437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1547
91.0%
152
 
8.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1547
91.0%
? 152
 
8.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1547
91.0%
? 152
 
8.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1547
91.0%
? 152
 
8.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1547
91.0%
? 152
 
8.9%
1 1
 
0.1%

ritm_ecg_p_07
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1195 
1
353 
?
152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1195
70.3%
1 353
 
20.8%
? 152
 
8.9%

Length

2024-11-19T17:25:28.938109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:29.149439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1195
70.3%
1 353
 
20.8%
152
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 1195
70.3%
1 353
 
20.8%
? 152
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1195
70.3%
1 353
 
20.8%
? 152
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1195
70.3%
1 353
 
20.8%
? 152
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1195
70.3%
1 353
 
20.8%
? 152
 
8.9%

ritm_ecg_p_08
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1502 
?
152 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1502
88.4%
? 152
 
8.9%
1 46
 
2.7%

Length

2024-11-19T17:25:29.386214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:29.601632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1502
88.4%
152
 
8.9%
1 46
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 1502
88.4%
? 152
 
8.9%
1 46
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1502
88.4%
? 152
 
8.9%
1 46
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1502
88.4%
? 152
 
8.9%
1 46
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1502
88.4%
? 152
 
8.9%
1 46
 
2.7%

n_r_ecg_p_01
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1527 
?
 
115
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1527
89.8%
? 115
 
6.8%
1 58
 
3.4%

Length

2024-11-19T17:25:29.843425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:30.054374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1527
89.8%
115
 
6.8%
1 58
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1527
89.8%
? 115
 
6.8%
1 58
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1527
89.8%
? 115
 
6.8%
1 58
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1527
89.8%
? 115
 
6.8%
1 58
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1527
89.8%
? 115
 
6.8%
1 58
 
3.4%

n_r_ecg_p_02
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1577 
?
 
115
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1577
92.8%
? 115
 
6.8%
1 8
 
0.5%

Length

2024-11-19T17:25:30.285968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:30.493068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1577
92.8%
115
 
6.8%
1 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1577
92.8%
? 115
 
6.8%
1 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1577
92.8%
? 115
 
6.8%
1 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1577
92.8%
? 115
 
6.8%
1 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1577
92.8%
? 115
 
6.8%
1 8
 
0.5%

n_r_ecg_p_03
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1381 
1
204 
?
 
115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1381
81.2%
1 204
 
12.0%
? 115
 
6.8%

Length

2024-11-19T17:25:30.713577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:30.929226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1381
81.2%
1 204
 
12.0%
115
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 1381
81.2%
1 204
 
12.0%
? 115
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1381
81.2%
1 204
 
12.0%
? 115
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1381
81.2%
1 204
 
12.0%
? 115
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1381
81.2%
1 204
 
12.0%
? 115
 
6.8%

n_r_ecg_p_04
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1516 
?
 
115
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1516
89.2%
? 115
 
6.8%
1 69
 
4.1%

Length

2024-11-19T17:25:31.154309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:31.363486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1516
89.2%
115
 
6.8%
1 69
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 1516
89.2%
? 115
 
6.8%
1 69
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1516
89.2%
? 115
 
6.8%
1 69
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1516
89.2%
? 115
 
6.8%
1 69
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1516
89.2%
? 115
 
6.8%
1 69
 
4.1%

n_r_ecg_p_05
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1515 
?
 
115
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1515
89.1%
? 115
 
6.8%
1 70
 
4.1%

Length

2024-11-19T17:25:31.582431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:31.790473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1515
89.1%
115
 
6.8%
1 70
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 1515
89.1%
? 115
 
6.8%
1 70
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1515
89.1%
? 115
 
6.8%
1 70
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1515
89.1%
? 115
 
6.8%
1 70
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1515
89.1%
? 115
 
6.8%
1 70
 
4.1%

n_r_ecg_p_06
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1553 
?
 
115
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Length

2024-11-19T17:25:32.035064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:32.239743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1553
91.4%
115
 
6.8%
1 32
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

n_r_ecg_p_08
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1581 
?
 
115
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1581
93.0%
? 115
 
6.8%
1 4
 
0.2%

Length

2024-11-19T17:25:32.470968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:32.681607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1581
93.0%
115
 
6.8%
1 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1581
93.0%
? 115
 
6.8%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1581
93.0%
? 115
 
6.8%
1 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1581
93.0%
? 115
 
6.8%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1581
93.0%
? 115
 
6.8%
1 4
 
0.2%

n_r_ecg_p_09
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1583 
?
 
115
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Length

2024-11-19T17:25:32.909238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:33.141761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1583
93.1%
115
 
6.8%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

n_r_ecg_p_10
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1583 
?
 
115
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Length

2024-11-19T17:25:33.371993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:33.579510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1583
93.1%
115
 
6.8%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

n_p_ecg_p_01
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1583 
?
 
115
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Length

2024-11-19T17:25:33.791830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:33.995676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1583
93.1%
115
 
6.8%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

n_p_ecg_p_03
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1553 
?
 
115
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Length

2024-11-19T17:25:34.245858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:34.453517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1553
91.4%
115
 
6.8%
1 32
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1553
91.4%
? 115
 
6.8%
1 32
 
1.9%

n_p_ecg_p_04
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1580 
?
 
115
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1580
92.9%
? 115
 
6.8%
1 5
 
0.3%

Length

2024-11-19T17:25:34.676755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:34.880793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1580
92.9%
115
 
6.8%
1 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1580
92.9%
? 115
 
6.8%
1 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1580
92.9%
? 115
 
6.8%
1 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1580
92.9%
? 115
 
6.8%
1 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1580
92.9%
? 115
 
6.8%
1 5
 
0.3%

n_p_ecg_p_05
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1583 
?
 
115
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Length

2024-11-19T17:25:35.117080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:35.320488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1583
93.1%
115
 
6.8%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1583
93.1%
? 115
 
6.8%
1 2
 
0.1%

n_p_ecg_p_06
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1558 
?
 
115
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1558
91.6%
? 115
 
6.8%
1 27
 
1.6%

Length

2024-11-19T17:25:35.552156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:35.758293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1558
91.6%
115
 
6.8%
1 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1558
91.6%
? 115
 
6.8%
1 27
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1558
91.6%
? 115
 
6.8%
1 27
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1558
91.6%
? 115
 
6.8%
1 27
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1558
91.6%
? 115
 
6.8%
1 27
 
1.6%

n_p_ecg_p_07
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1483 
?
 
115
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1483
87.2%
? 115
 
6.8%
1 102
 
6.0%

Length

2024-11-19T17:25:36.018109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:36.242016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1483
87.2%
115
 
6.8%
1 102
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 1483
87.2%
? 115
 
6.8%
1 102
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1483
87.2%
? 115
 
6.8%
1 102
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1483
87.2%
? 115
 
6.8%
1 102
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1483
87.2%
? 115
 
6.8%
1 102
 
6.0%

n_p_ecg_p_08
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1578 
?
 
115
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1578
92.8%
? 115
 
6.8%
1 7
 
0.4%

Length

2024-11-19T17:25:36.467729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:36.678052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1578
92.8%
115
 
6.8%
1 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1578
92.8%
? 115
 
6.8%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1578
92.8%
? 115
 
6.8%
1 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1578
92.8%
? 115
 
6.8%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1578
92.8%
? 115
 
6.8%
1 7
 
0.4%

n_p_ecg_p_09
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1575 
?
 
115
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1575
92.6%
? 115
 
6.8%
1 10
 
0.6%

Length

2024-11-19T17:25:36.898386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:37.105781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1575
92.6%
115
 
6.8%
1 10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1575
92.6%
? 115
 
6.8%
1 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1575
92.6%
? 115
 
6.8%
1 10
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1575
92.6%
? 115
 
6.8%
1 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1575
92.6%
? 115
 
6.8%
1 10
 
0.6%

n_p_ecg_p_10
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1551 
?
 
115
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1551
91.2%
? 115
 
6.8%
1 34
 
2.0%

Length

2024-11-19T17:25:37.355833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:37.565408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1551
91.2%
115
 
6.8%
1 34
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 1551
91.2%
? 115
 
6.8%
1 34
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1551
91.2%
? 115
 
6.8%
1 34
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1551
91.2%
? 115
 
6.8%
1 34
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1551
91.2%
? 115
 
6.8%
1 34
 
2.0%

n_p_ecg_p_11
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1557 
?
 
115
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1557
91.6%
? 115
 
6.8%
1 28
 
1.6%

Length

2024-11-19T17:25:37.781854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:37.986331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1557
91.6%
115
 
6.8%
1 28
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1557
91.6%
? 115
 
6.8%
1 28
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1557
91.6%
? 115
 
6.8%
1 28
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1557
91.6%
? 115
 
6.8%
1 28
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1557
91.6%
? 115
 
6.8%
1 28
 
1.6%

n_p_ecg_p_12
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1507 
?
 
115
1
 
78

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1507
88.6%
? 115
 
6.8%
1 78
 
4.6%

Length

2024-11-19T17:25:38.232106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:38.431314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1507
88.6%
115
 
6.8%
1 78
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 1507
88.6%
? 115
 
6.8%
1 78
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1507
88.6%
? 115
 
6.8%
1 78
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1507
88.6%
? 115
 
6.8%
1 78
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1507
88.6%
? 115
 
6.8%
1 78
 
4.6%

fibr_ter_01
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1677 
1
 
13
?
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Length

2024-11-19T17:25:38.717588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:39.095849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1677
98.6%
1 13
 
0.8%
? 10
 
0.6%

fibr_ter_02
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1674 
1
 
16
?
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1674
98.5%
1 16
 
0.9%
? 10
 
0.6%

Length

2024-11-19T17:25:39.508229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:39.746538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1674
98.5%
1 16
 
0.9%
10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1674
98.5%
1 16
 
0.9%
? 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1674
98.5%
1 16
 
0.9%
? 10
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1674
98.5%
1 16
 
0.9%
? 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1674
98.5%
1 16
 
0.9%
? 10
 
0.6%

fibr_ter_03
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1622 
1
 
68
?
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1622
95.4%
1 68
 
4.0%
? 10
 
0.6%

Length

2024-11-19T17:25:40.111558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:40.439857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1622
95.4%
1 68
 
4.0%
10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1622
95.4%
1 68
 
4.0%
? 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1622
95.4%
1 68
 
4.0%
? 10
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1622
95.4%
1 68
 
4.0%
? 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1622
95.4%
1 68
 
4.0%
? 10
 
0.6%

fibr_ter_05
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1686 
?
 
10
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1686
99.2%
? 10
 
0.6%
1 4
 
0.2%

Length

2024-11-19T17:25:40.886318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:41.287150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1686
99.2%
10
 
0.6%
1 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1686
99.2%
? 10
 
0.6%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1686
99.2%
? 10
 
0.6%
1 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1686
99.2%
? 10
 
0.6%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1686
99.2%
? 10
 
0.6%
1 4
 
0.2%

fibr_ter_06
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1681 
?
 
10
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1681
98.9%
? 10
 
0.6%
1 9
 
0.5%

Length

2024-11-19T17:25:42.185188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:42.480017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1681
98.9%
10
 
0.6%
1 9
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1681
98.9%
? 10
 
0.6%
1 9
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1681
98.9%
? 10
 
0.6%
1 9
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1681
98.9%
? 10
 
0.6%
1 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1681
98.9%
? 10
 
0.6%
1 9
 
0.5%

fibr_ter_07
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1684 
?
 
10
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1684
99.1%
? 10
 
0.6%
1 6
 
0.4%

Length

2024-11-19T17:25:42.956185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:43.344413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1684
99.1%
10
 
0.6%
1 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1684
99.1%
? 10
 
0.6%
1 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1684
99.1%
? 10
 
0.6%
1 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1684
99.1%
? 10
 
0.6%
1 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1684
99.1%
? 10
 
0.6%
1 6
 
0.4%

fibr_ter_08
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1688 
?
 
10
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1688
99.3%
? 10
 
0.6%
1 2
 
0.1%

Length

2024-11-19T17:25:43.577939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:43.786415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1688
99.3%
10
 
0.6%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1688
99.3%
? 10
 
0.6%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1688
99.3%
? 10
 
0.6%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1688
99.3%
? 10
 
0.6%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1688
99.3%
? 10
 
0.6%
1 2
 
0.1%

GIPO_K
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
797 
1
534 
?
369 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 797
46.9%
1 534
31.4%
? 369
21.7%

Length

2024-11-19T17:25:44.018069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:44.222913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 797
46.9%
1 534
31.4%
369
21.7%

Most occurring characters

ValueCountFrequency (%)
0 797
46.9%
1 534
31.4%
? 369
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 797
46.9%
1 534
31.4%
? 369
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 797
46.9%
1 534
31.4%
? 369
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 797
46.9%
1 534
31.4%
? 369
21.7%
Distinct52
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2024-11-19T17:25:44.464968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.3635294
Min length1

Characters and Unicode

Total characters4018
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.5%

Sample

1st row4.7
2nd row3.5
3rd row4
4th row3.9
5th row3.5
ValueCountFrequency (%)
371
21.8%
4 101
 
5.9%
3.8 91
 
5.4%
4.2 87
 
5.1%
3.9 79
 
4.6%
3.5 71
 
4.2%
4.1 62
 
3.6%
4.5 62
 
3.6%
4.3 60
 
3.5%
3.6 58
 
3.4%
Other values (42) 658
38.7%
2024-11-19T17:25:45.018471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1159
28.8%
4 726
18.1%
3 626
15.6%
? 371
 
9.2%
5 292
 
7.3%
2 160
 
4.0%
6 147
 
3.7%
8 145
 
3.6%
7 140
 
3.5%
9 131
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1159
28.8%
4 726
18.1%
3 626
15.6%
? 371
 
9.2%
5 292
 
7.3%
2 160
 
4.0%
6 147
 
3.7%
8 145
 
3.6%
7 140
 
3.5%
9 131
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1159
28.8%
4 726
18.1%
3 626
15.6%
? 371
 
9.2%
5 292
 
7.3%
2 160
 
4.0%
6 147
 
3.7%
8 145
 
3.6%
7 140
 
3.5%
9 131
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1159
28.8%
4 726
18.1%
3 626
15.6%
? 371
 
9.2%
5 292
 
7.3%
2 160
 
4.0%
6 147
 
3.7%
8 145
 
3.6%
7 140
 
3.5%
9 131
 
3.3%

GIPER_NA
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1295 
?
375 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1295
76.2%
? 375
 
22.1%
1 30
 
1.8%

Length

2024-11-19T17:25:45.289981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:45.491605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1295
76.2%
375
 
22.1%
1 30
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1295
76.2%
? 375
 
22.1%
1 30
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1295
76.2%
? 375
 
22.1%
1 30
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1295
76.2%
? 375
 
22.1%
1 30
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1295
76.2%
? 375
 
22.1%
1 30
 
1.8%

NA_BLOOD
Categorical

High correlation 

Distinct41
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
375 
136
214 
140
186 
130
125 
133
81 
Other values (36)
719 

Length

Max length3
Median length3
Mean length2.5588235
Min length1

Characters and Unicode

Total characters4350
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.3%

Sample

1st row138
2nd row132
3rd row132
4th row146
5th row132

Common Values

ValueCountFrequency (%)
? 375
22.1%
136 214
12.6%
140 186
10.9%
130 125
 
7.4%
133 81
 
4.8%
138 80
 
4.7%
143 63
 
3.7%
146 61
 
3.6%
134 57
 
3.4%
132 56
 
3.3%
Other values (31) 402
23.6%

Length

2024-11-19T17:25:45.745736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
375
22.1%
136 214
12.6%
140 186
10.9%
130 125
 
7.4%
133 81
 
4.8%
138 80
 
4.7%
143 63
 
3.7%
146 61
 
3.6%
134 57
 
3.4%
132 56
 
3.3%
Other values (31) 402
23.6%

Most occurring characters

ValueCountFrequency (%)
1 1388
31.9%
3 902
20.7%
4 516
 
11.9%
? 375
 
8.6%
0 340
 
7.8%
6 291
 
6.7%
2 223
 
5.1%
8 104
 
2.4%
5 84
 
1.9%
9 66
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1388
31.9%
3 902
20.7%
4 516
 
11.9%
? 375
 
8.6%
0 340
 
7.8%
6 291
 
6.7%
2 223
 
5.1%
8 104
 
2.4%
5 84
 
1.9%
9 66
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1388
31.9%
3 902
20.7%
4 516
 
11.9%
? 375
 
8.6%
0 340
 
7.8%
6 291
 
6.7%
2 223
 
5.1%
8 104
 
2.4%
5 84
 
1.9%
9 66
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1388
31.9%
3 902
20.7%
4 516
 
11.9%
? 375
 
8.6%
0 340
 
7.8%
6 291
 
6.7%
2 223
 
5.1%
8 104
 
2.4%
5 84
 
1.9%
9 66
 
1.5%
Distinct70
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2024-11-19T17:25:46.051601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.3288235
Min length1

Characters and Unicode

Total characters5659
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)1.7%

Sample

1st row?
2nd row0.38
3rd row0.3
4th row0.75
5th row0.45
ValueCountFrequency (%)
284
16.7%
0.15 230
13.5%
0.3 210
12.4%
0.45 178
10.5%
0.23 144
8.5%
0.38 126
7.4%
0.61 76
 
4.5%
0.75 73
 
4.3%
0.52 67
 
3.9%
0.9 35
 
2.1%
Other values (60) 277
16.3%
2024-11-19T17:25:46.663491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1415
25.0%
0 1343
23.7%
5 605
10.7%
3 514
 
9.1%
1 477
 
8.4%
2 297
 
5.2%
? 284
 
5.0%
4 209
 
3.7%
8 200
 
3.5%
6 155
 
2.7%
Other values (2) 160
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1415
25.0%
0 1343
23.7%
5 605
10.7%
3 514
 
9.1%
1 477
 
8.4%
2 297
 
5.2%
? 284
 
5.0%
4 209
 
3.7%
8 200
 
3.5%
6 155
 
2.7%
Other values (2) 160
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1415
25.0%
0 1343
23.7%
5 605
10.7%
3 514
 
9.1%
1 477
 
8.4%
2 297
 
5.2%
? 284
 
5.0%
4 209
 
3.7%
8 200
 
3.5%
6 155
 
2.7%
Other values (2) 160
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1415
25.0%
0 1343
23.7%
5 605
10.7%
3 514
 
9.1%
1 477
 
8.4%
2 297
 
5.2%
? 284
 
5.0%
4 209
 
3.7%
8 200
 
3.5%
6 155
 
2.7%
Other values (2) 160
 
2.8%
Distinct59
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2024-11-19T17:25:46.993366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.3870588
Min length1

Characters and Unicode

Total characters5758
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)1.2%

Sample

1st row?
2nd row0.18
3rd row0.11
4th row0.37
5th row0.22
ValueCountFrequency (%)
285
16.8%
0.15 271
15.9%
0.22 169
9.9%
0.07 149
8.8%
0.3 145
8.5%
0.11 113
 
6.6%
0.18 108
 
6.4%
0.37 81
 
4.8%
0.45 62
 
3.6%
0.26 55
 
3.2%
Other values (49) 262
15.4%
2024-11-19T17:25:47.584702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1575
27.4%
. 1415
24.6%
1 651
11.3%
2 448
 
7.8%
5 406
 
7.1%
3 315
 
5.5%
? 285
 
4.9%
7 274
 
4.8%
8 133
 
2.3%
4 133
 
2.3%
Other values (2) 123
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1575
27.4%
. 1415
24.6%
1 651
11.3%
2 448
 
7.8%
5 406
 
7.1%
3 315
 
5.5%
? 285
 
4.9%
7 274
 
4.8%
8 133
 
2.3%
4 133
 
2.3%
Other values (2) 123
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1575
27.4%
. 1415
24.6%
1 651
11.3%
2 448
 
7.8%
5 406
 
7.1%
3 315
 
5.5%
? 285
 
4.9%
7 274
 
4.8%
8 133
 
2.3%
4 133
 
2.3%
Other values (2) 123
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1575
27.4%
. 1415
24.6%
1 651
11.3%
2 448
 
7.8%
5 406
 
7.1%
3 315
 
5.5%
? 285
 
4.9%
7 274
 
4.8%
8 133
 
2.3%
4 133
 
2.3%
Other values (2) 123
 
2.1%

KFK_BLOOD
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
1696 
1.8
 
1
1.4
 
1
1.2
 
1
3.6
 
1

Length

Max length3
Median length1
Mean length1.0047059
Min length1

Characters and Unicode

Total characters1708
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 1696
99.8%
1.8 1
 
0.1%
1.4 1
 
0.1%
1.2 1
 
0.1%
3.6 1
 
0.1%

Length

2024-11-19T17:25:47.902704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:48.148291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1696
99.8%
1.8 1
 
0.1%
1.4 1
 
0.1%
1.2 1
 
0.1%
3.6 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
? 1696
99.3%
. 4
 
0.2%
1 3
 
0.2%
8 1
 
0.1%
4 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
6 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 1696
99.3%
. 4
 
0.2%
1 3
 
0.2%
8 1
 
0.1%
4 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
6 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 1696
99.3%
. 4
 
0.2%
1 3
 
0.2%
8 1
 
0.1%
4 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
6 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 1696
99.3%
. 4
 
0.2%
1 3
 
0.2%
8 1
 
0.1%
4 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
6 1
 
0.1%
Distinct175
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2024-11-19T17:25:48.508397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8782353
Min length1

Characters and Unicode

Total characters4893
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)2.5%

Sample

1st row8
2nd row7.8
3rd row10.8
4th row?
5th row8.3
ValueCountFrequency (%)
125
 
7.4%
6.9 32
 
1.9%
7 30
 
1.8%
8 30
 
1.8%
6.8 29
 
1.7%
7.4 29
 
1.7%
7.2 28
 
1.6%
9 27
 
1.6%
7.7 26
 
1.5%
7.5 25
 
1.5%
Other values (165) 1319
77.6%
2024-11-19T17:25:49.177368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1369
28.0%
1 663
13.5%
7 405
 
8.3%
6 401
 
8.2%
5 366
 
7.5%
9 353
 
7.2%
8 342
 
7.0%
4 291
 
5.9%
2 252
 
5.2%
3 199
 
4.1%
Other values (2) 252
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1369
28.0%
1 663
13.5%
7 405
 
8.3%
6 401
 
8.2%
5 366
 
7.5%
9 353
 
7.2%
8 342
 
7.0%
4 291
 
5.9%
2 252
 
5.2%
3 199
 
4.1%
Other values (2) 252
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1369
28.0%
1 663
13.5%
7 405
 
8.3%
6 401
 
8.2%
5 366
 
7.5%
9 353
 
7.2%
8 342
 
7.0%
4 291
 
5.9%
2 252
 
5.2%
3 199
 
4.1%
Other values (2) 252
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1369
28.0%
1 663
13.5%
7 405
 
8.3%
6 401
 
8.2%
5 366
 
7.5%
9 353
 
7.2%
8 342
 
7.0%
4 291
 
5.9%
2 252
 
5.2%
3 199
 
4.1%
Other values (2) 252
 
5.2%

ROE
Text

Distinct59
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2024-11-19T17:25:49.488846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.4658824
Min length1

Characters and Unicode

Total characters2492
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.5%

Sample

1st row16
2nd row3
3rd row?
4th row?
5th row?
ValueCountFrequency (%)
203
 
11.9%
5 134
 
7.9%
3 126
 
7.4%
10 104
 
6.1%
4 98
 
5.8%
7 91
 
5.4%
8 83
 
4.9%
6 79
 
4.6%
12 64
 
3.8%
15 63
 
3.7%
Other values (49) 655
38.5%
2024-11-19T17:25:50.088811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 513
20.6%
2 362
14.5%
3 273
11.0%
5 271
10.9%
? 203
 
8.1%
4 199
 
8.0%
0 194
 
7.8%
7 143
 
5.7%
8 132
 
5.3%
6 125
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 513
20.6%
2 362
14.5%
3 273
11.0%
5 271
10.9%
? 203
 
8.1%
4 199
 
8.0%
0 194
 
7.8%
7 143
 
5.7%
8 132
 
5.3%
6 125
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 513
20.6%
2 362
14.5%
3 273
11.0%
5 271
10.9%
? 203
 
8.1%
4 199
 
8.0%
0 194
 
7.8%
7 143
 
5.7%
8 132
 
5.3%
6 125
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 513
20.6%
2 362
14.5%
3 273
11.0%
5 271
10.9%
? 203
 
8.1%
4 199
 
8.0%
0 194
 
7.8%
7 143
 
5.7%
8 132
 
5.3%
6 125
 
5.0%

TIME_B_S
Categorical

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2
360 
9
269 
1
198 
3
175 
6
151 
Other values (5)
547 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row2
5th row9

Common Values

ValueCountFrequency (%)
2 360
21.2%
9 269
15.8%
1 198
11.6%
3 175
10.3%
6 151
8.9%
7 141
 
8.3%
? 126
 
7.4%
8 101
 
5.9%
5 92
 
5.4%
4 87
 
5.1%

Length

2024-11-19T17:25:50.382598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:50.649355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 360
21.2%
9 269
15.8%
1 198
11.6%
3 175
10.3%
6 151
8.9%
7 141
 
8.3%
126
 
7.4%
8 101
 
5.9%
5 92
 
5.4%
4 87
 
5.1%

Most occurring characters

ValueCountFrequency (%)
2 360
21.2%
9 269
15.8%
1 198
11.6%
3 175
10.3%
6 151
8.9%
7 141
 
8.3%
? 126
 
7.4%
8 101
 
5.9%
5 92
 
5.4%
4 87
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 360
21.2%
9 269
15.8%
1 198
11.6%
3 175
10.3%
6 151
8.9%
7 141
 
8.3%
? 126
 
7.4%
8 101
 
5.9%
5 92
 
5.4%
4 87
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 360
21.2%
9 269
15.8%
1 198
11.6%
3 175
10.3%
6 151
8.9%
7 141
 
8.3%
? 126
 
7.4%
8 101
 
5.9%
5 92
 
5.4%
4 87
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 360
21.2%
9 269
15.8%
1 198
11.6%
3 175
10.3%
6 151
8.9%
7 141
 
8.3%
? 126
 
7.4%
8 101
 
5.9%
5 92
 
5.4%
4 87
 
5.1%

R_AB_1_n
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1282 
1
298 
2
 
78
3
 
26
?
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1282
75.4%
1 298
 
17.5%
2 78
 
4.6%
3 26
 
1.5%
? 16
 
0.9%

Length

2024-11-19T17:25:50.956234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:51.180241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1282
75.4%
1 298
 
17.5%
2 78
 
4.6%
3 26
 
1.5%
16
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1282
75.4%
1 298
 
17.5%
2 78
 
4.6%
3 26
 
1.5%
? 16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1282
75.4%
1 298
 
17.5%
2 78
 
4.6%
3 26
 
1.5%
? 16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1282
75.4%
1 298
 
17.5%
2 78
 
4.6%
3 26
 
1.5%
? 16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1282
75.4%
1 298
 
17.5%
2 78
 
4.6%
3 26
 
1.5%
? 16
 
0.9%

R_AB_2_n
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1414 
1
 
133
?
 
108
2
 
44
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1414
83.2%
1 133
 
7.8%
? 108
 
6.4%
2 44
 
2.6%
3 1
 
0.1%

Length

2024-11-19T17:25:51.420279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:51.645025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1414
83.2%
1 133
 
7.8%
108
 
6.4%
2 44
 
2.6%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1414
83.2%
1 133
 
7.8%
? 108
 
6.4%
2 44
 
2.6%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1414
83.2%
1 133
 
7.8%
? 108
 
6.4%
2 44
 
2.6%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1414
83.2%
1 133
 
7.8%
? 108
 
6.4%
2 44
 
2.6%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1414
83.2%
1 133
 
7.8%
? 108
 
6.4%
2 44
 
2.6%
3 1
 
0.1%

R_AB_3_n
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1469 
?
 
128
1
 
86
2
 
15
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1469
86.4%
? 128
 
7.5%
1 86
 
5.1%
2 15
 
0.9%
3 2
 
0.1%

Length

2024-11-19T17:25:51.897468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:52.123800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1469
86.4%
128
 
7.5%
1 86
 
5.1%
2 15
 
0.9%
3 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1469
86.4%
? 128
 
7.5%
1 86
 
5.1%
2 15
 
0.9%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1469
86.4%
? 128
 
7.5%
1 86
 
5.1%
2 15
 
0.9%
3 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1469
86.4%
? 128
 
7.5%
1 86
 
5.1%
2 15
 
0.9%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1469
86.4%
? 128
 
7.5%
1 86
 
5.1%
2 15
 
0.9%
3 2
 
0.1%

NA_KB
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
657 
1
618 
0
425 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row1
3rd row1
4th row?
5th row0

Common Values

ValueCountFrequency (%)
? 657
38.6%
1 618
36.4%
0 425
25.0%

Length

2024-11-19T17:25:52.355073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:52.570144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
657
38.6%
1 618
36.4%
0 425
25.0%

Most occurring characters

ValueCountFrequency (%)
? 657
38.6%
1 618
36.4%
0 425
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 657
38.6%
1 618
36.4%
0 425
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 657
38.6%
1 618
36.4%
0 425
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 657
38.6%
1 618
36.4%
0 425
25.0%

NOT_NA_KB
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
701 
?
686 
0
313 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row0
3rd row1
4th row?
5th row0

Common Values

ValueCountFrequency (%)
1 701
41.2%
? 686
40.4%
0 313
18.4%

Length

2024-11-19T17:25:52.802847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:53.049625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 701
41.2%
686
40.4%
0 313
18.4%

Most occurring characters

ValueCountFrequency (%)
1 701
41.2%
? 686
40.4%
0 313
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 701
41.2%
? 686
40.4%
0 313
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 701
41.2%
? 686
40.4%
0 313
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 701
41.2%
? 686
40.4%
0 313
18.4%

LID_KB
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
?
677 
0
627 
1
396 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row1
3rd row1
4th row?
5th row0

Common Values

ValueCountFrequency (%)
? 677
39.8%
0 627
36.9%
1 396
23.3%

Length

2024-11-19T17:25:53.275101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:53.587489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
677
39.8%
0 627
36.9%
1 396
23.3%

Most occurring characters

ValueCountFrequency (%)
? 677
39.8%
0 627
36.9%
1 396
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 677
39.8%
0 627
36.9%
1 396
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 677
39.8%
0 627
36.9%
1 396
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 677
39.8%
0 627
36.9%
1 396
23.3%

NITR_S
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1496 
1
195 
?
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1496
88.0%
1 195
 
11.5%
? 9
 
0.5%

Length

2024-11-19T17:25:54.101914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:54.444168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1496
88.0%
1 195
 
11.5%
9
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1496
88.0%
1 195
 
11.5%
? 9
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1496
88.0%
1 195
 
11.5%
? 9
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1496
88.0%
1 195
 
11.5%
? 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1496
88.0%
1 195
 
11.5%
? 9
 
0.5%

NA_R_1_n
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1108 
1
409 
2
132 
3
 
35
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1108
65.2%
1 409
 
24.1%
2 132
 
7.8%
3 35
 
2.1%
4 11
 
0.6%
? 5
 
0.3%

Length

2024-11-19T17:25:54.879956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:55.294384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1108
65.2%
1 409
 
24.1%
2 132
 
7.8%
3 35
 
2.1%
4 11
 
0.6%
5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1108
65.2%
1 409
 
24.1%
2 132
 
7.8%
3 35
 
2.1%
4 11
 
0.6%
? 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1108
65.2%
1 409
 
24.1%
2 132
 
7.8%
3 35
 
2.1%
4 11
 
0.6%
? 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1108
65.2%
1 409
 
24.1%
2 132
 
7.8%
3 35
 
2.1%
4 11
 
0.6%
? 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1108
65.2%
1 409
 
24.1%
2 132
 
7.8%
3 35
 
2.1%
4 11
 
0.6%
? 5
 
0.3%

NA_R_2_n
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1474 
?
 
108
1
 
87
2
 
30
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1474
86.7%
? 108
 
6.4%
1 87
 
5.1%
2 30
 
1.8%
3 1
 
0.1%

Length

2024-11-19T17:25:55.781055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:56.226684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1474
86.7%
108
 
6.4%
1 87
 
5.1%
2 30
 
1.8%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1474
86.7%
? 108
 
6.4%
1 87
 
5.1%
2 30
 
1.8%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
? 108
 
6.4%
1 87
 
5.1%
2 30
 
1.8%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
? 108
 
6.4%
1 87
 
5.1%
2 30
 
1.8%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
? 108
 
6.4%
1 87
 
5.1%
2 30
 
1.8%
3 1
 
0.1%

NA_R_3_n
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1493 
?
 
131
1
 
60
2
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1493
87.8%
? 131
 
7.7%
1 60
 
3.5%
2 16
 
0.9%

Length

2024-11-19T17:25:56.632933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:56.967751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1493
87.8%
131
 
7.7%
1 60
 
3.5%
2 16
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1493
87.8%
? 131
 
7.7%
1 60
 
3.5%
2 16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1493
87.8%
? 131
 
7.7%
1 60
 
3.5%
2 16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1493
87.8%
? 131
 
7.7%
1 60
 
3.5%
2 16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1493
87.8%
? 131
 
7.7%
1 60
 
3.5%
2 16
 
0.9%

NOT_NA_1_n
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1237 
1
376 
2
 
53
3
 
17
?
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1237
72.8%
1 376
 
22.1%
2 53
 
3.1%
3 17
 
1.0%
? 10
 
0.6%
4 7
 
0.4%

Length

2024-11-19T17:25:57.385964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:57.811143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1237
72.8%
1 376
 
22.1%
2 53
 
3.1%
3 17
 
1.0%
10
 
0.6%
4 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1237
72.8%
1 376
 
22.1%
2 53
 
3.1%
3 17
 
1.0%
? 10
 
0.6%
4 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1237
72.8%
1 376
 
22.1%
2 53
 
3.1%
3 17
 
1.0%
? 10
 
0.6%
4 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1237
72.8%
1 376
 
22.1%
2 53
 
3.1%
3 17
 
1.0%
? 10
 
0.6%
4 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1237
72.8%
1 376
 
22.1%
2 53
 
3.1%
3 17
 
1.0%
? 10
 
0.6%
4 7
 
0.4%

NOT_NA_2_n
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1454 
?
 
110
1
 
95
2
 
38
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1454
85.5%
? 110
 
6.5%
1 95
 
5.6%
2 38
 
2.2%
3 3
 
0.2%

Length

2024-11-19T17:25:58.169992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:58.392999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1454
85.5%
110
 
6.5%
1 95
 
5.6%
2 38
 
2.2%
3 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1454
85.5%
? 110
 
6.5%
1 95
 
5.6%
2 38
 
2.2%
3 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1454
85.5%
? 110
 
6.5%
1 95
 
5.6%
2 38
 
2.2%
3 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1454
85.5%
? 110
 
6.5%
1 95
 
5.6%
2 38
 
2.2%
3 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1454
85.5%
? 110
 
6.5%
1 95
 
5.6%
2 38
 
2.2%
3 3
 
0.2%

NOT_NA_3_n
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1474 
?
 
131
1
 
57
2
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1474
86.7%
? 131
 
7.7%
1 57
 
3.4%
2 38
 
2.2%

Length

2024-11-19T17:25:58.628376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:58.840624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1474
86.7%
131
 
7.7%
1 57
 
3.4%
2 38
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 1474
86.7%
? 131
 
7.7%
1 57
 
3.4%
2 38
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
? 131
 
7.7%
1 57
 
3.4%
2 38
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
? 131
 
7.7%
1 57
 
3.4%
2 38
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
? 131
 
7.7%
1 57
 
3.4%
2 38
 
2.2%

LID_S_n
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1211 
1
479 
?
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1211
71.2%
1 479
 
28.2%
? 10
 
0.6%

Length

2024-11-19T17:25:59.062706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:59.280980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1211
71.2%
1 479
 
28.2%
10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1211
71.2%
1 479
 
28.2%
? 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1211
71.2%
1 479
 
28.2%
? 10
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1211
71.2%
1 479
 
28.2%
? 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1211
71.2%
1 479
 
28.2%
? 10
 
0.6%

B_BLOK_S_n
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1474 
1
215 
?
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1474
86.7%
1 215
 
12.6%
? 11
 
0.6%

Length

2024-11-19T17:25:59.509162image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:25:59.719750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1474
86.7%
1 215
 
12.6%
11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1474
86.7%
1 215
 
12.6%
? 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
1 215
 
12.6%
? 11
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
1 215
 
12.6%
? 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1474
86.7%
1 215
 
12.6%
? 11
 
0.6%

ANT_CA_S_n
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
1125 
0
562 
?
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1125
66.2%
0 562
33.1%
? 13
 
0.8%

Length

2024-11-19T17:25:59.935197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:00.135127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1125
66.2%
0 562
33.1%
13
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 1125
66.2%
0 562
33.1%
? 13
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1125
66.2%
0 562
33.1%
? 13
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1125
66.2%
0 562
33.1%
? 13
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1125
66.2%
0 562
33.1%
? 13
 
0.8%

GEPAR_S_n
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
1203 
0
480 
?
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1203
70.8%
0 480
 
28.2%
? 17
 
1.0%

Length

2024-11-19T17:26:00.366974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:00.571882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1203
70.8%
0 480
 
28.2%
17
 
1.0%

Most occurring characters

ValueCountFrequency (%)
1 1203
70.8%
0 480
 
28.2%
? 17
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1203
70.8%
0 480
 
28.2%
? 17
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1203
70.8%
0 480
 
28.2%
? 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1203
70.8%
0 480
 
28.2%
? 17
 
1.0%

ASP_S_n
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
1252 
0
431 
?
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1252
73.6%
0 431
 
25.4%
? 17
 
1.0%

Length

2024-11-19T17:26:00.786793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:00.989144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1252
73.6%
0 431
 
25.4%
17
 
1.0%

Most occurring characters

ValueCountFrequency (%)
1 1252
73.6%
0 431
 
25.4%
? 17
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1252
73.6%
0 431
 
25.4%
? 17
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1252
73.6%
0 431
 
25.4%
? 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1252
73.6%
0 431
 
25.4%
? 17
 
1.0%

TIKL_S_n
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1654 
1
 
30
?
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1654
97.3%
1 30
 
1.8%
? 16
 
0.9%

Length

2024-11-19T17:26:01.243858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:01.442063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1654
97.3%
1 30
 
1.8%
16
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1654
97.3%
1 30
 
1.8%
? 16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1654
97.3%
1 30
 
1.8%
? 16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1654
97.3%
1 30
 
1.8%
? 16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1654
97.3%
1 30
 
1.8%
? 16
 
0.9%

TRENT_S_n
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1343 
1
341 
?
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1343
79.0%
1 341
 
20.1%
? 16
 
0.9%

Length

2024-11-19T17:26:02.125355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:02.350165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1343
79.0%
1 341
 
20.1%
16
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1343
79.0%
1 341
 
20.1%
? 16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1343
79.0%
1 341
 
20.1%
? 16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1343
79.0%
1 341
 
20.1%
? 16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1343
79.0%
1 341
 
20.1%
? 16
 
0.9%

FIBR_PREDS
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1530 
1
170 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1530
90.0%
1 170
 
10.0%

Length

2024-11-19T17:26:02.577224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:02.767612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1530
90.0%
1 170
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 1530
90.0%
1 170
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1530
90.0%
1 170
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1530
90.0%
1 170
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1530
90.0%
1 170
 
10.0%

PREDS_TAH
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1680 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1680
98.8%
1 20
 
1.2%

Length

2024-11-19T17:26:02.980963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:03.181007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1680
98.8%
1 20
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1680
98.8%
1 20
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
1 20
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
1 20
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1680
98.8%
1 20
 
1.2%

JELUD_TAH
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1658 
1
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1658
97.5%
1 42
 
2.5%

Length

2024-11-19T17:26:03.419290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:03.618053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1658
97.5%
1 42
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1658
97.5%
1 42
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1658
97.5%
1 42
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1658
97.5%
1 42
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1658
97.5%
1 42
 
2.5%

FIBR_JELUD
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1629 
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1629
95.8%
1 71
 
4.2%

Length

2024-11-19T17:26:03.825301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:04.023560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1629
95.8%
1 71
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 1629
95.8%
1 71
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1629
95.8%
1 71
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1629
95.8%
1 71
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1629
95.8%
1 71
 
4.2%

A_V_BLOK
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1643 
1
 
57

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1643
96.6%
1 57
 
3.4%

Length

2024-11-19T17:26:04.243787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:04.460044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1643
96.6%
1 57
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1643
96.6%
1 57
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1643
96.6%
1 57
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1643
96.6%
1 57
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1643
96.6%
1 57
 
3.4%

OTEK_LANC
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1541 
1
159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Length

2024-11-19T17:26:04.667002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:04.867093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

RAZRIV
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1646 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1646
96.8%
1 54
 
3.2%

Length

2024-11-19T17:26:05.077900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:05.284888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1646
96.8%
1 54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1646
96.8%
1 54
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1646
96.8%
1 54
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1646
96.8%
1 54
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1646
96.8%
1 54
 
3.2%

DRESSLER
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1625 
1
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1625
95.6%
1 75
 
4.4%

Length

2024-11-19T17:26:05.528900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:05.734247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1625
95.6%
1 75
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 1625
95.6%
1 75
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1625
95.6%
1 75
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1625
95.6%
1 75
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1625
95.6%
1 75
 
4.4%

ZSN
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1306 
1
394 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1306
76.8%
1 394
 
23.2%

Length

2024-11-19T17:26:05.948285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:06.195046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1306
76.8%
1 394
 
23.2%

Most occurring characters

ValueCountFrequency (%)
0 1306
76.8%
1 394
 
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1306
76.8%
1 394
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1306
76.8%
1 394
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1306
76.8%
1 394
 
23.2%

REC_IM
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1541 
1
159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Length

2024-11-19T17:26:06.427272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:06.635448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1541
90.6%
1 159
 
9.4%

P_IM_STEN
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1552 
1
 
148

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1552
91.3%
1 148
 
8.7%

Length

2024-11-19T17:26:06.848857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T17:26:07.048252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1552
91.3%
1 148
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 1552
91.3%
1 148
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1552
91.3%
1 148
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1552
91.3%
1 148
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1552
91.3%
1 148
 
8.7%

LET_IS
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47705882
Minimum0
Maximum7
Zeros1429
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2024-11-19T17:26:07.230825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3818178
Coefficient of variation (CV)2.8965355
Kurtosis10.754681
Mean0.47705882
Median Absolute Deviation (MAD)0
Skewness3.3388229
Sum811
Variance1.9094204
MonotonicityNot monotonic
2024-11-19T17:26:07.455579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1429
84.1%
1 110
 
6.5%
3 54
 
3.2%
7 27
 
1.6%
6 27
 
1.6%
4 23
 
1.4%
2 18
 
1.1%
5 12
 
0.7%
ValueCountFrequency (%)
0 1429
84.1%
1 110
 
6.5%
2 18
 
1.1%
3 54
 
3.2%
4 23
 
1.4%
5 12
 
0.7%
6 27
 
1.6%
7 27
 
1.6%
ValueCountFrequency (%)
7 27
 
1.6%
6 27
 
1.6%
5 12
 
0.7%
4 23
 
1.4%
3 54
 
3.2%
2 18
 
1.1%
1 110
 
6.5%
0 1429
84.1%

Interactions

2024-11-19T17:24:53.906277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T17:24:53.429671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T17:24:54.149343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-19T17:24:53.675486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-19T17:26:07.874979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ANT_CA_S_nASP_S_nA_V_BLOKB_BLOK_S_nDLIT_AGDRESSLERD_AD_KBRIGD_AD_ORITFIBR_JELUDFIBR_PREDSFIB_G_POSTFK_STENOKGBGEPAR_S_nGIPER_NAGIPO_KGT_POSTIBS_NASLIBS_POSTIDIM_PG_PINF_ANAMJELUD_TAHKFK_BLOODK_SH_POSTLET_ISLID_KBLID_S_nMP_TP_POSTNA_BLOODNA_KBNA_R_1_nNA_R_2_nNA_R_3_nNITR_SNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nNOT_NA_KBOTEK_LANCO_L_POSTPREDS_TAHP_IM_STENRAZRIVREC_IMR_AB_1_nR_AB_2_nR_AB_3_nSEXSIM_GIPERTSTENOK_ANSVT_POSTS_AD_KBRIGS_AD_ORITTIKL_S_nTIME_B_STRENT_S_nZSNZSN_Aant_imendocr_01endocr_02endocr_03fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08inf_imlat_imn_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10np_01np_04np_05np_07np_08np_09np_10nr_01nr_02nr_03nr_04nr_07nr_08nr_11post_imritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06
ANT_CA_S_n1.0000.5750.0700.6570.0500.0000.0670.1270.0000.0270.0210.0680.0350.5690.0170.0000.0000.0000.0940.1340.0000.0000.0570.0000.1180.1300.0590.5570.0000.0000.0410.4360.1830.1730.5890.4330.2040.1900.0220.0290.0370.0000.0300.0000.0290.0660.1560.1510.0000.0000.0760.0000.0340.1130.5870.0480.5870.0610.0600.0640.0190.0000.0000.0350.0000.0160.0000.0000.0000.1330.0700.0440.0000.0500.0000.0240.0750.0290.0670.0000.0000.0000.0000.0000.0110.0000.0080.0000.0040.0000.0000.0000.0430.0580.0440.0470.0430.0430.0400.0470.0460.0460.0520.0470.0470.0510.0410.0040.0000.0570.0000.0210.0000.0170.0360.0000.0000.000
ASP_S_n0.5751.0000.0480.5680.0390.0210.1080.1620.0000.0000.0000.0990.0550.6910.0340.0370.0000.0000.0780.1370.0000.0000.0000.0000.1540.1550.0000.4870.0000.0000.0230.3820.1960.1780.5130.3760.2090.1850.0190.0050.0000.0000.0000.0490.0000.0720.1700.1510.0000.0310.0920.0000.0720.2320.6460.0560.6620.0460.0740.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.1150.0570.0310.0000.0000.0000.0370.0680.0370.0580.0040.0000.0000.0500.0000.0000.0000.0000.0000.0460.0350.0000.0000.0720.0730.0710.0720.0750.0720.0730.0790.0770.0770.0850.0770.0790.0780.0330.0290.0270.0440.0000.0000.0050.0360.0120.0000.0220.000
A_V_BLOK0.0700.0481.0000.0090.0520.0000.0690.0000.0450.0000.0740.0000.0730.0000.0000.0000.0540.0000.0650.1150.0000.0110.0600.0000.0640.0730.0000.0390.0560.0860.0370.0530.0410.0570.0000.0000.0890.0420.0000.0000.0710.0000.0460.0440.0000.0260.0440.0520.0000.0000.0420.0540.0000.0000.0000.0000.0000.0000.0000.1320.0000.0000.0000.0000.0460.0080.0000.0250.0400.0000.2330.1330.0000.0880.0370.0820.1820.0000.0000.0000.0000.0000.0410.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.1440.0000.0200.0000.0000.0380.0000.0000.000
B_BLOK_S_n0.6570.5680.0091.0000.0470.0000.0000.0550.0350.0180.0000.0180.0120.5670.0000.0000.0000.0400.0540.1200.0000.0000.0000.0000.0240.0320.0000.6060.0000.0370.0650.4740.1190.1080.6400.4700.1600.1450.0470.0000.0350.0250.0460.0320.0000.0370.0410.0470.0710.0060.0000.0000.0000.0000.5850.0640.5860.0480.0350.0850.0490.0000.0000.0700.0000.0000.0000.0050.0000.1460.0930.0590.0000.0000.0000.0000.0180.0100.0710.0000.0260.0000.0000.0240.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0430.0000.0000.0710.0170.0000.0000.0000.0000.0000.0190.0000.0000.0000.000
DLIT_AG0.0500.0390.0520.0471.0000.0110.0780.1230.0670.0380.0680.0780.4720.0000.0300.0290.0410.0670.1140.0950.0330.0550.0340.0170.0900.0450.0640.0000.0660.0030.0740.0360.0150.0390.0560.0440.0690.0530.0780.0750.0750.0540.0780.0860.0600.0330.0250.0000.3240.1410.1220.0990.0260.1310.0260.0320.0000.0000.0770.0520.1260.0570.0480.0000.0270.0410.0000.0390.0250.0000.0290.0570.0000.0000.0840.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0160.0200.0000.0780.0000.0000.1230.1040.1150.1040.1090.1050.1040.1220.1280.1170.1220.1120.1130.1170.0200.0860.0230.0000.0000.0580.0220.0000.0580.0000.0000.000
DRESSLER0.0000.0210.0000.0000.0111.0000.0320.0000.0000.0000.0000.0440.0700.0160.0150.0000.0000.0000.0650.2590.0000.0750.0180.0000.0230.0350.0000.0360.0000.1530.0080.0470.0340.0390.0000.0000.0260.0300.0000.0000.0000.0000.0000.0000.0000.0830.0320.0440.0220.0000.0570.0000.0410.1100.0000.0210.0270.0490.0460.0470.0510.0000.0380.0000.0270.1290.0000.0000.0000.0000.0260.0510.0000.0180.0000.0720.0000.0000.0000.0000.0000.0140.0000.0000.0100.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.000
D_AD_KBRIG0.0670.1080.0690.0000.0780.0321.0000.1670.0360.0570.0000.0860.0890.0930.0000.0200.2490.0000.1540.0770.0000.1870.0000.0000.3700.1150.3010.0000.0000.0000.2810.0000.1580.1800.0430.0000.1570.1800.3280.0590.0610.1310.0000.0000.0590.1590.1550.1490.0910.1170.0680.1480.5130.1550.0000.0500.0420.0320.1360.0830.1040.1360.0920.0000.0000.0000.0000.0000.0920.0000.0830.0920.0910.0780.0200.1610.0370.0000.0000.0000.0770.0000.0000.0000.0000.0000.0530.0000.1100.2340.0000.0000.2440.2430.2630.2410.2430.2420.2430.2190.2390.2290.2580.2240.2420.2600.0360.0470.0310.1370.0000.0220.0000.1490.1520.1570.1450.133
D_AD_ORIT0.1270.1620.0000.0550.1230.0000.1671.0000.0410.1090.0940.1410.1750.1190.0810.0660.0910.1490.2190.1990.0520.1420.0000.0000.5350.2260.0690.0000.0890.1590.0620.0690.2610.3030.0150.0000.2480.2760.1000.1040.1370.1080.0000.0690.1030.1490.2550.2570.0770.1360.1040.0370.1590.5060.0000.0310.0000.0740.2080.0980.0870.0840.0510.0000.0000.0380.0000.0240.1160.0000.1210.1020.0690.1320.0750.1040.1850.0840.0600.0910.0560.0510.1400.0990.0520.1020.0560.0960.1470.0820.0640.0670.2860.2850.3040.2860.2960.2890.3020.4390.2680.2630.2680.2580.2710.2610.0630.1400.1070.1450.0240.0970.0620.0840.1390.1490.0820.073
FIBR_JELUD0.0000.0000.0450.0350.0670.0000.0360.0411.0000.0580.0850.0870.0900.0310.0000.0810.0490.0050.0360.1680.0380.0400.1250.1060.0430.2760.0000.2130.0500.0750.0000.1320.1230.0580.0790.0000.0440.0250.0000.0000.0400.0000.0300.0000.0310.0720.0920.0750.0030.0000.0000.0420.0000.0500.0210.0600.0000.0000.0200.0680.0260.0000.0000.0000.0270.0540.0000.0580.0210.0000.0000.0290.0000.0720.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0320.0000.0110.0370.0000.1610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0400.0000.0000.0180.0000.0730.0000.0000.000
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NOT_NA_3_n0.1900.1850.0420.1450.0530.0300.1800.2760.0250.0690.0600.1440.0960.1240.0580.0530.0000.0160.1740.3330.0760.0870.0000.0000.3590.4300.0450.1260.0440.0000.0420.1360.5220.5840.1420.2930.6631.0000.0340.0000.1260.0590.0840.2100.0890.1740.4950.6030.0750.0990.1130.0000.1760.2670.1060.1040.1020.1600.1740.1480.0730.0730.0570.0000.0000.0140.0000.0000.0380.0000.1540.1460.0840.0550.0560.0500.0970.0460.0370.0350.0480.0430.0930.0170.0210.0120.0230.0450.0370.0240.0120.0120.2030.2050.2040.2030.2060.2030.2050.1860.1880.1860.1870.1850.1910.1880.1230.1060.0690.0800.0370.0830.0490.0540.0500.0450.0530.049
NOT_NA_KB0.0220.0190.0000.0470.0780.0000.3280.1000.0000.0370.0000.0280.0180.0520.0130.0470.0000.0160.0590.1660.0000.0100.0000.0000.0240.0710.6820.0970.0150.0680.6670.0550.0420.0000.0000.0820.0540.0341.0000.0400.0350.0000.0840.0000.0000.0920.0050.0160.0000.0000.0160.0300.3250.0520.0000.1160.0390.0500.0510.0590.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0140.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0190.0130.0110.0000.0000.0000.0190.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.000
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O_L_POST0.0370.0000.0710.0350.0750.0000.0610.1370.0400.0000.7070.1020.0710.0390.0000.0470.7070.0000.1160.1640.0190.0850.0000.0000.6380.1330.0410.0000.6550.0000.0450.0580.1520.1360.2080.0110.1710.1260.0350.1101.0000.0590.0540.0000.0000.0320.1460.1350.0820.0210.0820.7120.0000.1000.0000.0900.0000.0000.1260.0900.0350.0000.0290.0000.0000.0100.0000.0120.0000.0000.0670.0920.0000.0000.0860.0350.0220.0140.0630.0460.0240.0000.0000.0000.0000.0000.0310.0190.0000.0550.0000.0000.0540.0540.0600.0540.0550.0540.0630.1060.0470.0560.0460.0450.0450.0460.0640.0980.0000.0430.0000.1150.0130.0300.0250.0280.0570.028
PREDS_TAH0.0000.0000.0000.0250.0540.0000.1310.1080.0000.0780.0450.0000.0000.0050.0000.0000.0450.0000.0000.0550.0000.0000.0260.0000.0370.0570.0000.0000.0370.0280.0000.0580.0000.0000.0000.0370.0000.0590.0000.0000.0591.0000.0000.0000.0000.0290.0000.0000.0000.0220.0000.2360.2080.0350.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0460.0480.0460.0460.0480.0460.0470.0470.0480.0480.0460.0150.0120.0110.0120.0230.0170.2210.0110.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0380.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.000
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REC_IM0.0290.0000.0000.0000.0600.0000.0590.1030.0310.0290.0270.0930.0000.0440.0020.0000.0630.0000.0670.2800.0000.0280.0400.0590.0430.1490.0000.0000.0000.0600.0000.0800.1330.1530.0590.0000.0840.0890.0000.1690.0000.0000.0100.0001.0000.0660.1260.1930.0580.0000.1240.0070.0000.0000.0000.0780.0240.0810.0610.0450.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0580.0580.0600.0580.0610.0620.0600.0570.0590.0700.0600.0580.0570.0650.0580.0570.0710.0590.0750.0580.1020.0000.0000.0000.0000.0000.0140.0280.0250.0540.0160.0130.0190.0280.0000.0360.0460.0290.0240.0660.0220.0500.0710.0000.0000.000
R_AB_1_n0.0660.0720.0260.0370.0330.0830.1590.1490.0720.0570.0000.0860.0450.0570.0610.0640.0000.0310.0630.1340.0300.1440.0000.0000.1710.1410.0310.0610.0000.0420.0550.3330.1910.1830.0620.2000.1860.1740.0920.1070.0320.0290.0000.0870.0661.0000.2130.2010.0440.0380.0810.0000.0710.1030.0260.0460.0100.0590.0730.0430.0500.0630.0440.0150.0610.0580.0280.0470.0710.0180.0600.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1550.1560.1580.1550.1560.1650.1740.1430.1440.1490.1440.1420.1430.1450.0270.0320.0000.0000.0000.0390.0000.0700.0750.0690.0670.074
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SEX0.0000.0000.0000.0710.3240.0220.0910.0770.0030.0980.0000.1070.2580.0170.0000.0410.0000.0170.1200.1340.0000.0530.0330.0180.0000.1400.0000.0970.0710.0320.0000.0000.0150.0730.0470.0610.0550.0750.0000.0580.0820.0000.0000.0670.0580.0440.0070.0701.0000.0560.0900.0000.1330.1870.0000.0290.0490.0930.1160.0430.2630.1120.1070.0500.0320.1070.0000.0110.0410.0170.0720.0410.0290.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0180.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0580.0000.0350.0310.0000.0000.0000.0310.1030.0650.0270.0150.0970.0170.0000.0000.0000.0300.000
SIM_GIPERT0.0000.0310.0000.0060.1410.0000.1170.1360.0000.0000.0000.1300.6860.0280.0000.0000.0000.0000.1320.0960.0880.2500.0000.0000.1000.1290.0000.0000.0000.0000.0000.0000.0910.0980.0000.0000.0800.0990.0000.0000.0210.0220.0000.0500.0000.0380.0860.0970.0561.0000.1020.0000.1120.1990.0000.0190.0000.0000.2000.0740.2280.2420.2330.0000.0000.0000.0000.0000.0000.0000.0660.0730.0000.0000.0330.0000.0400.0620.0000.0000.0140.0440.0000.0060.0000.0130.0240.0200.0000.0000.0000.0000.3500.3500.3500.3500.3500.3500.3500.3230.3240.3230.3240.3230.3230.3240.0700.0420.0000.0420.0000.0080.0220.2800.2800.2820.2810.280
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TIKL_S_n0.5870.6460.0000.5850.0260.0000.0000.0000.0210.0000.0000.0000.0000.6420.0000.0000.0000.0340.0000.0560.0000.0000.0000.0000.0000.0000.0000.5010.0000.0000.0000.3920.1510.0770.5290.3860.1160.1060.0000.0000.0000.0000.0600.0000.0000.0260.1220.0810.0000.0000.0000.0000.0000.1411.0000.0130.7080.0520.0000.0000.0000.0000.0000.0000.0000.0700.0000.0160.0340.1190.0440.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.000
TIME_B_S0.0480.0560.0000.0640.0320.0210.0500.0310.0600.0000.0820.0000.0000.1130.0450.0810.0900.0110.0600.0810.0700.0410.0640.0000.1090.0800.1090.1130.0560.0040.1890.0930.0860.1040.0560.0370.0900.1040.1160.0610.0900.0000.0450.1140.0780.0460.0730.0870.0290.0190.0000.0760.0000.0350.0131.0000.0230.0410.0540.0710.0410.0000.0160.0330.0550.0870.0000.0240.0000.0000.0650.0510.0360.0590.0370.0270.0690.0490.0410.0560.0520.0480.0410.0250.0420.0560.0550.0430.0500.0420.0490.0400.0000.0720.0240.0000.0000.0110.0260.0530.0470.0210.0250.0210.0250.0450.0470.0280.0000.0380.0380.0490.0230.0000.0040.0290.0000.030
TRENT_S_n0.5870.6620.0000.5860.0000.0270.0420.0000.0000.0000.0000.0000.0000.6420.0520.0480.0000.0000.0190.1530.0230.0000.0000.0000.0000.0000.0540.5010.0000.0490.0510.3930.0820.0690.5290.3890.1210.1020.0390.0260.0000.0000.0620.0000.0240.0100.0000.0000.0490.0000.0260.0000.0870.1590.7080.0231.0000.0520.0000.0300.0370.0000.0000.0000.0000.0140.0000.0240.0000.1220.0350.0530.0000.0000.0000.0000.0000.0000.0570.0000.0000.0040.0270.0000.0000.0460.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0620.0280.0230.0250.0220.0240.0260.0400.0000.0050.0000.000
ZSN0.0610.0460.0000.0480.0000.0490.0320.0740.0000.0900.0000.0230.0590.0520.0000.0000.0000.0570.0460.1110.0000.0000.0000.0110.0760.2390.0210.0380.0810.0000.0000.0340.1750.1950.0320.1140.1470.1600.0500.1200.0000.0000.0360.0590.0810.0590.1360.1650.0930.0000.0470.0000.0000.0910.0520.0410.0521.0000.3860.0670.1290.0380.0300.0040.0000.0210.0000.0090.0290.0110.0000.1030.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0240.0110.0000.0300.0950.0000.0000.0000.0340.0350.0360.0290.0420.0680.0510.0610.0240.0300.0890.0460.0290.0230.0000.0530.0910.0240.0300.0000.0000.1090.0000.0000.0000.000
ZSN_A0.0600.0740.0000.0350.0770.0460.1360.2080.0200.0000.0640.1510.1340.0700.0370.0400.0000.0000.2300.2520.0000.1390.0000.0790.2070.2210.0540.0370.1350.0000.0500.0310.1470.1700.1120.0210.1470.1740.0510.0680.1260.0000.0300.0840.0610.0730.1360.1490.1160.2000.1310.0160.1210.1830.0000.0540.0000.3861.0000.1010.1420.1210.1070.0000.0000.0170.0000.0000.0000.0000.1020.1060.0000.0510.0180.0000.0590.0000.0500.0000.0370.0000.0860.0240.0130.0410.0670.0620.1320.0610.0000.0000.3440.3430.3340.3550.3310.3310.3510.3670.3390.3350.3680.3300.3390.3320.0900.1470.1170.0230.0610.0960.0000.1370.1460.1570.1340.136
ant_im0.0640.0370.1320.0850.0520.0470.0830.0980.0680.0590.0520.0660.0810.0550.0730.0710.0800.0200.1060.1500.0530.1010.0870.0000.1160.1330.0790.1330.0700.0690.1230.0850.1170.1480.0940.0420.1110.1480.0590.0520.0900.0000.0960.1210.0450.0430.1050.1130.0430.0740.0710.0250.0550.0900.0000.0710.0300.0670.1011.0000.0280.0000.0120.0000.0520.0180.0300.0000.0000.0000.5150.5350.1110.1160.1270.1230.1370.1550.1170.1230.1230.1190.1370.1260.1220.1320.1390.1200.1190.1180.1190.1190.1070.1090.1170.1110.1160.1090.1380.1270.1140.1170.1230.1360.1130.1250.4350.1210.0970.1120.0920.1480.1060.0000.0110.0350.0000.075
endocr_010.0190.0000.0000.0490.1260.0510.1040.0870.0260.0000.0000.1310.2200.0000.0200.0000.0000.0000.0940.1010.0000.2210.0000.0000.0520.1830.0110.0550.0000.0630.0070.0350.0910.0990.0500.0000.0880.0730.0000.0860.0350.0410.0000.0000.0530.0500.0930.0780.2630.2280.0950.0000.1170.1400.0000.0410.0370.1290.1420.0281.0000.6750.6740.0000.0000.0610.0000.0000.0000.0000.0710.0000.0150.0260.0000.0000.0420.0460.0000.0070.0050.0000.0000.0090.0000.0090.0220.0000.0000.0000.0000.0000.1940.1950.1940.1940.1960.1940.1950.1890.1790.1790.1790.1790.1790.1790.0280.0680.0180.0170.0450.0690.0360.4020.4010.4010.4000.400
endocr_020.0000.0000.0000.0000.0570.0000.1360.0840.0000.0000.0000.0920.2190.0000.0310.0000.0000.0000.0530.0880.1070.2250.0000.0960.0500.1780.0000.0110.0000.0430.0000.0570.0650.0720.0000.0000.0650.0730.0000.0000.0000.0000.0100.0730.0000.0630.0640.0620.1120.2420.0720.0000.1290.0910.0000.0000.0000.0380.1210.0000.6751.0000.7070.0000.0000.0000.0000.0000.0200.0000.0310.0000.0690.0000.0000.0000.0370.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2050.2130.2050.2050.2050.2050.2050.1890.1890.1930.1890.1880.1950.1890.0000.0450.0140.0000.1040.0130.0000.4200.4200.4220.4200.420
endocr_030.0000.0000.0000.0000.0480.0380.0920.0510.0000.0000.0000.0870.2190.0000.0350.0150.0000.0000.0530.0580.0000.2180.0000.0000.0480.1600.0190.0150.0000.0130.0000.0000.0610.0560.0000.0000.0640.0570.0000.0000.0290.0000.0000.0000.0000.0440.0620.0530.1070.2330.0630.0000.0920.1120.0000.0160.0000.0300.1070.0120.6740.7071.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0260.0480.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.2040.2040.2040.2040.2040.2040.2040.1880.1880.1880.1880.1880.1880.1880.0000.0290.0000.0000.0000.0250.0200.4200.4200.4210.4200.420
fibr_ter_010.0350.0000.0000.0700.0000.0000.0000.0000.0000.0190.0000.0000.0480.0200.0250.0310.0000.0000.0000.0770.0000.0000.0200.0000.0000.0000.0000.0100.0000.1070.0000.0720.0000.0000.0000.0150.0000.0000.0000.0200.0000.0000.0110.0000.0000.0150.0000.0000.0500.0000.0000.0000.0000.0000.0000.0330.0000.0040.0000.0000.0000.0000.0001.0000.7070.7070.7070.7070.7070.7070.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.1550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_020.0000.0000.0460.0000.0270.0270.0000.0000.0270.0000.0000.0000.0600.0380.0000.0100.0000.0000.0000.0380.0210.0000.0280.0000.0350.0000.0000.0400.0000.0000.0000.0860.0000.0000.0000.0000.0320.0000.0000.0240.0000.0000.0240.0000.0000.0610.0000.0000.0320.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0520.0000.0000.0000.7071.0000.7070.7070.7070.7070.7070.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0140.0450.0000.0000.0000.1550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0460.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_030.0160.0000.0080.0000.0410.1290.0000.0380.0540.0000.0000.0450.0930.0480.0300.0410.0000.0000.0740.1100.0000.0850.1050.0000.0510.0000.0030.0720.0240.0000.0250.0880.0150.0000.0000.0000.0320.0140.0000.0140.0100.0000.0190.0000.0000.0580.0060.0000.1070.0000.0600.0000.0000.0470.0700.0870.0140.0210.0170.0180.0610.0000.0000.7070.7071.0000.7070.7070.7070.7070.0270.0450.0030.0120.0310.0030.0160.0130.0090.0120.0100.0070.0180.0190.0100.0430.0160.0250.0220.0060.1570.0030.0000.0000.0000.0000.0000.0000.0300.0000.0000.0140.0000.0000.0000.0000.0860.0000.0310.0000.0000.0000.0350.0000.0000.0000.0000.000
fibr_ter_050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0030.0000.0000.0000.0000.0000.0300.0000.0000.0170.0000.0000.0000.0000.0430.0000.0000.0000.1320.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.7070.7070.7071.0000.7070.7070.7070.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.000
fibr_ter_060.0000.0000.0250.0050.0390.0000.0000.0240.0580.0000.0450.0050.0490.0000.0000.0000.0000.0000.0000.0670.0000.0000.0430.0000.0140.0000.0280.0000.0000.0000.0480.1210.2340.0380.0000.0000.0000.0000.0220.0120.0120.0000.0000.0000.0000.0470.2330.1600.0110.0000.0230.0000.0870.0000.0160.0240.0240.0090.0000.0000.0000.0000.0000.7070.7070.7070.7071.0000.7070.7070.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0450.0000.0000.0000.0000.1540.0000.0380.0380.0380.0380.0380.0380.0380.0320.0320.0440.0330.0310.0320.0440.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_070.0000.0000.0400.0000.0250.0000.0920.1160.0210.0000.0000.0000.0530.0000.0000.0000.0000.0400.0260.0000.0090.0000.0570.0000.0280.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0710.0000.0000.0410.0000.0000.0000.0000.0000.0340.0000.0000.0290.0000.0000.0000.0200.0000.7070.7070.7070.7070.7071.0000.7070.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0040.0280.0000.0000.0000.0000.000
fibr_ter_080.1330.1150.0000.1460.0000.0000.0000.0000.0000.0000.0000.0300.0460.1180.0000.0000.0000.0000.0000.0050.0000.0000.0160.0000.0000.0000.0000.1540.0000.0000.0000.0970.0000.0000.1630.0000.0000.0000.0000.0440.0000.0000.0440.0000.0000.0180.0000.0000.0170.0000.0430.0000.0000.0000.1190.0000.1220.0110.0000.0000.0000.0000.0000.7070.7070.7070.7070.7070.7071.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0020.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.000
inf_im0.0700.0570.2330.0930.0290.0260.0830.1210.0000.0130.0380.0410.0540.0370.0240.0220.0490.0280.1050.1490.1000.0930.0500.0000.1630.1370.0520.0370.0820.0000.0640.0690.1300.1540.0750.0000.1250.1540.0140.0000.0670.0000.0480.0650.0000.0600.1220.1300.0720.0660.0520.0550.0600.1210.0440.0650.0350.0000.1020.5150.0710.0310.0440.0000.0000.0270.0000.0000.0000.0001.0000.4510.0500.0860.0540.0820.1220.0910.0480.0560.0690.0520.0580.1040.0590.0520.0570.0680.0580.0830.0820.0990.0760.0770.0770.0720.0780.0730.1120.0920.0910.0940.1070.1160.0910.0900.4570.0820.0810.1030.0670.0980.0850.0430.0000.0000.0000.021
lat_im0.0440.0310.1330.0590.0570.0510.0920.1020.0290.0420.0440.0460.0820.0400.0730.0710.0490.0160.0960.1380.0340.0970.0560.0000.1170.1310.0000.0570.0740.0400.0360.0440.1080.1420.0720.0340.1160.1460.0000.0570.0920.0550.0520.1080.0330.0610.1030.1140.0410.0730.0510.0200.0460.0830.0000.0510.0530.1030.1060.5350.0000.0000.0000.0340.0420.0450.0800.0460.0340.0280.4511.0000.0410.0570.0500.0640.0860.0970.0410.0560.0610.0470.0820.0670.0600.0630.0650.0670.0500.0470.0440.0540.1170.1090.1130.1080.1100.1070.1380.1200.1280.1210.1230.1390.1220.1190.3980.0860.0590.0650.0430.1200.0660.0210.0000.0330.0000.028
n_p_ecg_p_010.0000.0000.0000.0000.0000.0000.0910.0690.0000.0000.0000.0000.0000.0000.0270.0140.0250.0000.0000.0980.0000.0650.0000.0420.0140.0130.0130.0000.0170.0860.0280.0310.0000.0340.0000.0080.0000.0840.0130.0270.0000.0460.0670.0350.0580.0000.0000.0490.0290.0000.0000.0000.0000.0370.0000.0360.0000.0000.0000.1110.0150.0690.0000.0000.0000.0030.0000.0000.0000.0000.0500.0411.0000.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.6960.6930.6940.6930.6930.6930.6930.6930.6930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.5790.5780.5780.5780.5800.5780.0000.0000.0000.0000.000
n_p_ecg_p_030.0500.0000.0880.0000.0000.0180.0780.1320.0720.0360.0860.0000.0080.0000.0270.0200.0270.0000.0000.1130.0000.0730.0830.0420.0160.0750.0100.0000.0180.0290.0280.0000.0230.0520.0000.0000.0320.0550.0000.0250.0000.0480.0770.0800.0580.0000.0370.0630.0000.0000.0590.0800.0000.0810.0000.0590.0000.0000.0510.1160.0260.0000.0000.0000.0000.0120.0000.0000.0000.0000.0860.0570.7071.0000.7070.7070.7070.7070.7070.7070.7070.7070.7070.6950.6940.6940.6940.6940.6940.6960.6930.6930.0800.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0340.0480.5780.5790.5780.5780.5790.5780.0000.0000.0000.0000.000
n_p_ecg_p_040.0000.0000.0370.0000.0840.0000.0200.0750.0000.0000.0850.0000.0830.0000.0240.0110.0920.0000.0000.1000.0000.0710.0000.0420.0910.0530.0150.0000.0840.0000.0180.0390.0430.0570.0000.0000.0470.0560.0000.0320.0860.0460.0700.0360.0600.0000.0490.0670.0000.0330.0240.0860.0000.0540.0000.0370.0000.0000.0180.1270.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0540.0500.7070.7071.0000.7070.7070.7070.7070.7070.7070.7070.7070.6940.6930.6940.6940.6940.6940.6930.6930.6930.0630.0630.0630.0630.0650.0630.0630.0620.0940.0600.0570.0570.0570.0570.0110.5790.5780.5780.5780.5790.5780.0000.0000.0000.0990.000
n_p_ecg_p_050.0240.0370.0820.0000.0000.0720.1610.1040.0000.0000.0000.0340.0000.0270.0520.0460.0250.0000.0550.0980.0000.0650.0000.0420.0720.0430.0120.0080.0170.0000.0130.0000.0300.0510.0090.0000.0360.0500.0000.0270.0350.0460.0670.0350.0580.0000.0380.0620.0000.0000.0350.0000.1420.0930.0000.0270.0000.0000.0000.1230.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0820.0640.7070.7070.7071.0000.7070.7070.7070.7070.7070.7070.7070.6960.6930.6940.6930.6930.6930.6930.6930.6930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.5780.5780.5780.5780.5780.5780.0000.0350.0000.0000.000
n_p_ecg_p_060.0750.0680.1820.0180.0000.0000.0370.1850.0000.0000.0230.1220.0390.0000.0280.0180.0420.0000.1770.1260.0780.0940.0620.0420.1270.1110.0210.0260.0550.0000.0120.0000.1110.0920.0140.0000.1010.0970.0000.0340.0220.0480.0760.0660.0610.0000.0980.1000.0000.0400.1070.0250.0600.2330.0000.0690.0000.0000.0590.1370.0420.0370.0260.0000.0000.0160.0000.0000.0000.0000.1220.0860.7070.7070.7070.7071.0000.7070.7070.7070.7070.7070.7070.6940.6940.6940.6940.6940.6950.6940.6940.6940.0450.0450.0450.0440.0470.0450.0450.0460.0470.0440.0560.0380.0390.0400.0380.5900.5800.7510.5940.5800.5780.0480.0680.0350.0340.035
n_p_ecg_p_070.0290.0370.0000.0100.0000.0000.0000.0840.0000.0000.0250.0000.0660.0200.0270.0180.0390.0000.0000.0960.0710.0800.0210.0430.0300.0320.0090.0350.0370.0000.0360.0550.0240.0440.0250.0110.0430.0460.0000.0410.0140.0460.0660.0350.0620.0000.0230.0540.0000.0620.0000.0230.0000.0820.0000.0490.0000.0000.0000.1550.0460.0470.0480.0000.0000.0130.0000.0000.0000.0000.0910.0970.7070.7070.7070.7070.7071.0000.7070.7070.7070.7070.7080.6940.6950.6940.6940.6930.6940.6940.6940.6940.0000.0060.0730.0000.0160.0000.0330.0000.0000.0290.0000.0610.0000.0000.0410.5800.5790.5790.5790.5820.5800.0100.0000.0180.0000.000
n_p_ecg_p_080.0670.0580.0000.0710.0000.0000.0000.0600.0000.0030.0000.0620.0000.0550.0340.0360.0260.0000.0000.0990.0000.0640.0350.0420.0160.0760.0280.0820.0200.0000.0310.0000.0000.0370.0830.0420.0000.0370.0000.0320.0630.0470.0680.0530.0600.0000.0000.0530.0000.0000.0240.0000.0000.0660.0570.0410.0570.0000.0500.1170.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0480.0410.7070.7070.7070.7070.7070.7071.0000.7070.7070.7070.7070.6940.6930.6940.6940.6960.6940.6930.6930.6930.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.0000.0000.0000.0000.0490.5790.5780.5780.5780.5790.5780.0000.0000.0000.0000.042
n_p_ecg_p_090.0000.0040.0000.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0340.0260.0260.0000.0460.1010.0000.0740.0000.0420.0170.0450.0000.0220.0180.0000.0000.0270.0230.0620.0160.0000.0000.0350.0000.0380.0460.0470.0740.0380.0570.0000.0170.0730.0000.0000.0000.0000.0000.0000.0000.0560.0000.0190.0000.1230.0070.0000.0000.0000.0000.0120.0000.0000.0000.0000.0560.0560.7070.7070.7070.7070.7070.7070.7071.0000.7070.7070.7070.6950.6930.6940.6940.6940.6940.6930.6930.6930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0160.5780.5780.5780.5780.5780.5780.0130.0000.0000.0000.000
n_p_ecg_p_100.0000.0000.0000.0260.0200.0000.0770.0560.0000.0110.0000.0000.0140.0000.0330.0170.0270.0000.0000.0980.0000.0670.0000.1270.0230.0530.0000.0000.0700.1200.0190.0220.0360.0520.0510.0300.0400.0480.0000.0370.0240.0480.0710.0350.0590.0000.0380.0600.0430.0140.0000.0000.1330.0790.0000.0520.0000.0000.0370.1230.0050.0000.0000.0000.0000.0100.0000.0000.0000.0000.0690.0610.7070.7070.7070.7070.7070.7070.7070.7071.0000.7070.7070.6930.6940.6940.6950.6940.6940.6930.6930.6930.0000.0000.0600.1170.1940.0000.0000.0000.0160.0180.0000.0000.0000.0540.0250.5840.5810.5780.5780.5800.5790.0000.0000.0000.0690.000
n_p_ecg_p_110.0000.0000.0000.0000.0000.0140.0000.0510.0000.0370.0000.0190.0240.0000.0270.0140.0270.0000.0000.1030.0000.0640.0000.0420.0220.0310.0000.0100.0170.0000.0280.0000.0180.0410.0000.0000.0310.0430.0000.0340.0000.0480.0720.0350.0700.0000.0330.0490.0000.0440.0000.0000.0000.0720.0000.0480.0040.0000.0000.1190.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0520.0470.7070.7070.7070.7070.7070.7070.7070.7070.7071.0000.7070.6940.6940.6940.6940.6940.6940.6930.6930.6930.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0280.0000.0000.0000.1140.5780.5780.5780.5780.5780.5780.0000.0000.0000.0000.000
n_p_ecg_p_120.0000.0500.0410.0000.0000.0000.0000.1400.0570.0240.0000.0000.0000.0000.0320.0170.0290.0000.0180.1330.0350.0840.0000.0430.0620.1470.0000.0200.0240.0000.0000.0480.0530.0740.0000.0000.0590.0930.0000.0380.0000.0460.0700.0350.0600.0000.0580.0830.0000.0000.0000.0000.0780.1060.0000.0410.0270.0000.0860.1370.0000.0000.0000.0000.0140.0180.0000.0350.0000.0450.0580.0820.7070.7070.7070.7070.7070.7080.7070.7070.7070.7071.0000.6940.6940.6940.6950.6940.6940.6940.6940.6950.0000.0280.0000.0000.0000.1070.0280.0270.0000.0000.0000.0000.0090.0000.0370.5790.5790.5780.5780.5780.5780.0000.0000.0000.0000.000
n_r_ecg_p_010.0000.0000.0850.0240.0000.0000.0000.0990.0000.0880.0000.0000.0000.0000.0300.0170.0280.0000.0000.0920.0170.0700.0000.0420.0140.0240.0100.0280.0270.0190.0080.0000.0000.0270.0000.0000.0000.0170.0000.0350.0000.0150.0730.0400.0580.0000.0000.0430.0000.0060.0000.0000.0000.0880.0000.0250.0000.0000.0240.1260.0090.0000.0000.0000.0000.0190.0000.0000.0000.0000.1040.0670.6960.6950.6940.6960.6940.6940.6940.6950.6930.6940.6941.0000.7070.7090.7070.7070.7070.7070.7070.7070.0000.0000.0000.0000.0000.0540.0380.0390.0030.0200.0000.0000.0290.0000.0630.5780.5790.5790.5780.5780.5780.0390.0000.0000.0000.000
n_r_ecg_p_020.0110.0000.0000.0000.0000.0100.0000.0520.0000.0000.0000.0000.0000.0000.0350.0250.0260.0000.0000.0990.0000.0640.0000.0420.0120.0350.0150.0200.0230.0000.0090.0000.0000.0240.0000.0000.0000.0210.0000.0450.0000.0120.0780.0370.0570.0000.0000.0430.0000.0000.0000.0830.0000.0840.0000.0420.0000.0240.0130.1220.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0590.0600.6930.6940.6930.6930.6940.6950.6930.6930.6940.6940.6940.7071.0000.7070.7070.7070.7070.7070.7070.7070.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.5780.5790.5790.5780.5790.5780.0090.0270.0000.0000.000
n_r_ecg_p_030.0000.0000.0000.0190.0000.0000.0000.1020.0000.0000.0000.0410.0000.0000.0320.0210.0310.0000.0360.0990.0000.0670.0470.0440.0110.0310.0490.1390.0570.0700.0490.0630.0000.0280.0000.0000.0000.0120.0400.0250.0000.0110.0830.0360.0650.0000.0000.0440.0180.0130.0390.0000.0000.0850.0000.0560.0460.0110.0410.1320.0090.0000.0000.0750.0140.0430.0000.0450.0050.0020.0520.0630.6940.6940.6940.6940.6940.6940.6940.6940.6940.6940.6940.7090.7071.0000.7090.7090.7080.7070.7070.7070.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0110.0330.0000.0000.0300.5790.5810.5790.5800.5790.5790.0640.0000.0000.0000.000
n_r_ecg_p_040.0080.0000.0000.0000.0160.0190.0530.0560.0320.0180.0220.0170.0000.0000.0390.0260.0360.0000.0000.1140.0000.0730.0750.0430.0140.0700.0440.1290.0370.0510.0400.0170.0000.0280.0000.0000.0000.0230.0000.0280.0310.0120.0690.0360.0580.0000.0000.0480.0000.0240.0000.0000.0710.0400.0180.0550.0000.0000.0670.1390.0220.0000.0260.0000.0450.0160.0000.0000.0000.0000.0570.0650.6930.6940.6940.6930.6940.6940.6940.6940.6950.6940.6950.7070.7070.7091.0000.7070.7070.7070.7070.7070.0000.0000.0000.0000.0000.0000.0000.0000.0090.0110.0000.0000.0000.0110.0170.5780.5800.5790.5780.5790.5780.0260.0450.0030.0000.000
n_r_ecg_p_050.0000.0000.0000.0000.0200.0000.0000.0960.0000.2080.0480.0250.0000.0000.0380.0320.0600.0000.0000.1330.0000.0730.0000.0430.0200.1090.0120.0270.4060.0000.0000.0370.0000.0580.0400.0170.0000.0450.0190.0370.0190.0230.0720.0440.0570.0000.0070.0490.0920.0200.0000.0000.1240.0870.0000.0430.0000.0300.0620.1200.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0680.0670.6930.6940.6940.6930.6940.6930.6960.6940.6940.6940.6940.7070.7070.7090.7071.0000.7070.7070.7070.7070.0520.0000.0350.0000.0000.0000.0000.0060.0280.1440.0740.0000.0000.0670.0360.6050.7310.5790.5780.5800.5780.0260.0360.0760.0000.000
n_r_ecg_p_060.0040.0460.0000.0000.0000.0000.1100.1470.0110.0230.0000.0580.0000.0200.0350.0330.0470.0000.0460.1050.1210.0710.0000.1310.0340.0890.0130.0190.3320.0000.0240.0150.0000.0330.0000.0000.0000.0370.0130.0800.0000.0170.0710.0410.0710.0000.0000.0430.0000.0000.0500.0000.0580.1040.0000.0500.0040.0950.1320.1190.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0580.0500.6930.6940.6940.6930.6950.6940.6940.6940.6940.6940.6940.7070.7070.7080.7070.7071.0000.7070.7070.7070.0000.0000.0000.0000.0000.0000.0610.0000.0030.0150.4600.0000.0000.0000.0180.5960.6970.5790.5780.5810.5780.0000.0000.0000.0000.000
n_r_ecg_p_080.0000.0350.0000.0000.0780.0000.2340.0820.0370.0620.0000.0000.0000.0000.0370.0290.0250.0000.0000.0980.0000.0650.0000.0420.0100.0580.0030.0170.0200.0000.0000.0000.0000.0270.0000.1030.0000.0240.0110.0360.0550.2210.0680.0360.0590.0000.0000.0450.0000.0000.0000.3740.1190.0690.0000.0420.0000.0000.0610.1180.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0830.0470.6930.6960.6930.6930.6940.6940.6930.6930.6930.6930.6940.7070.7070.7070.7070.7070.7071.0000.7070.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1000.0920.5790.5790.5830.5790.5790.5790.0000.0000.0430.0000.000
n_r_ecg_p_090.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0320.0200.1780.0000.0000.0930.0000.0630.1000.0420.1280.0000.0180.0210.0190.0000.0150.0300.0000.0180.0000.0000.0000.0120.0000.0540.0000.0110.0670.0350.0750.0000.0000.0400.0000.0000.0000.0000.0000.0850.0000.0490.0000.0000.0000.1190.0000.0000.0000.1550.1550.1570.1540.1540.1540.1540.0820.0440.6930.6930.6930.6930.6940.6940.6930.6930.6930.6930.6940.7070.7070.7070.7070.7070.7070.7071.0000.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5780.5780.5780.5780.5780.5780.0000.0000.0000.0000.000
n_r_ecg_p_100.0000.0000.0000.0000.0000.0000.0000.0670.1610.0410.0000.0560.0000.0000.0330.0250.0250.0000.0000.0930.0630.0630.0000.0420.0090.0000.0090.0420.0460.0000.0150.0300.0000.0180.0000.0000.0290.0120.0000.0540.0000.0110.0670.0350.0580.0000.0150.0400.0000.0000.0260.0000.0000.0000.0000.0400.0000.0000.0000.1190.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0990.0540.6930.6930.6930.6930.6940.6940.6930.6930.6930.6930.6950.7070.7070.7070.7070.7070.7070.7070.7071.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.5800.5820.5800.5800.5800.5800.0000.0000.0000.0000.000
np_010.0430.0720.0000.0000.1230.0000.2440.2860.0000.0120.0140.2620.3850.0320.0000.0000.0430.0000.3190.1540.0490.2490.0000.0000.2110.2020.0000.0000.0280.0000.0150.0000.2140.2030.0380.0000.1960.2030.0000.0000.0540.0000.0000.0860.1020.1550.2100.2050.0000.3500.2150.0000.2330.2580.0000.0000.0000.0340.3440.1070.1940.2050.2040.0000.0000.0000.0000.0380.0000.0000.0760.1170.0000.0800.0630.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0001.0000.7070.7070.7070.7070.7070.7070.6170.6270.6170.6170.6170.6170.6170.0420.0360.0000.0000.0000.0150.0000.2480.2470.2470.2470.247
np_040.0580.0730.0000.0000.1040.0000.2430.2850.0000.0150.0140.2650.3850.0340.0060.0180.0430.0000.3200.1530.0720.2500.0000.0000.2110.1940.0000.0000.0000.0000.0370.0000.1970.2050.0420.0000.1930.2050.0190.0140.0540.0000.0040.0000.0000.1560.1960.2070.0000.3500.2160.0000.2350.2590.0000.0720.0000.0350.3430.1090.1950.2130.2040.0000.0000.0000.0000.0380.0000.0000.0770.1090.0000.0000.0630.0000.0450.0060.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.7071.0000.7070.7070.7070.7070.7070.6170.6170.6170.6170.6170.6490.6170.0470.0360.0000.0000.0000.0070.0000.2480.2470.2470.2470.247
np_050.0440.0710.0000.0000.1150.0000.2630.3040.0000.0040.0530.2610.3850.0290.0000.0060.0430.0000.3190.1630.0500.2510.0000.0000.2110.1950.0000.0000.0350.0000.0300.0000.1950.2040.0390.0000.1910.2040.0000.0000.0600.0000.0240.0000.0000.1580.1950.2060.0000.3500.2130.0640.2480.2700.0640.0240.0000.0360.3340.1170.1940.2050.2040.0000.0000.0000.0000.0380.0000.0000.0770.1130.0000.0000.0630.0000.0450.0730.0000.0000.0600.0000.0000.0000.0640.0000.0000.0350.0000.0000.0000.0000.7070.7071.0000.7070.7070.7070.7070.6170.6180.6170.6180.6520.6170.6170.0450.0470.0480.0000.0000.0160.0000.2470.2470.2470.2470.247
np_070.0470.0720.0000.0000.1040.0000.2410.2860.0000.0090.0140.2610.4390.0400.0000.0100.0430.0000.3190.1550.0490.2480.0000.0000.2110.1910.0000.0000.0000.0000.0260.0000.1940.2030.0370.0000.1900.2030.0130.0000.0540.0000.0000.0000.0000.1550.1930.2050.0000.3500.2110.0000.2280.2590.0000.0000.0000.0290.3550.1110.1940.2050.2040.0000.0000.0000.0000.0380.0000.0000.0720.1080.0000.0000.0630.0000.0440.0000.0000.0000.1170.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.7070.7070.7071.0000.7070.7070.7070.6170.6170.6170.6170.6170.6170.6170.0410.0650.0470.0510.0440.0540.0450.2480.2470.2470.2470.247
np_080.0430.0750.0000.0000.1090.0000.2430.2960.0000.0200.0150.2610.3860.0350.0240.0280.0430.0000.3190.1600.0490.2470.0450.0000.2140.2030.0000.0000.0000.0000.0130.0000.1950.2060.0400.0000.1910.2060.0000.0000.0550.0000.0110.0000.0000.1560.1990.2070.0190.3500.2110.0000.2310.2690.0000.0000.0000.0420.3310.1160.1960.2050.2040.0000.0000.0000.0000.0380.0000.0000.0780.1100.0000.0000.0650.0000.0470.0160.0000.0000.1940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7070.7070.7070.7071.0000.7070.7070.6170.6170.6180.6170.6170.6170.6170.0450.0380.0000.0000.0000.0060.0000.2480.2470.2470.2470.247
np_090.0430.0720.0000.0000.1050.0000.2420.2890.0000.0120.0140.3010.3850.0320.0060.0130.0430.0000.3190.1630.0830.2580.0000.0000.2110.1910.0000.0000.0000.0000.0140.0680.2000.2030.0380.0000.1900.2030.0000.0000.0540.0000.0000.0000.0000.1650.2060.2050.0000.3500.2120.0000.2290.2610.0000.0110.0000.0680.3310.1090.1940.2050.2040.0000.0000.0000.0000.0380.0000.0000.0730.1070.0000.0000.0630.0000.0450.0000.0000.0000.0000.0000.1070.0540.0000.0000.0000.0000.0000.0000.0000.0000.7070.7070.7070.7070.7071.0000.7070.6170.6170.6170.6170.6170.6170.6170.0420.0360.0000.0000.0000.0150.0000.2480.2470.2470.2470.247
np_100.0400.0730.0000.0000.1040.0000.2430.3020.0000.0150.0140.2660.3850.0380.0280.0300.0430.0000.3230.1600.0490.2480.0000.0000.2180.2110.0000.0000.1020.0000.0270.0000.1970.2050.0420.0000.1930.2050.0000.0140.0630.0000.0340.0000.0140.1740.1960.2070.0000.3500.2170.0000.2330.2820.0000.0260.0000.0510.3510.1380.1950.2050.2040.0000.0000.0300.0000.0380.0000.0000.1120.1380.0000.0000.0630.0000.0450.0330.0000.0000.0000.0000.0280.0380.0000.0000.0000.0000.0610.0000.0000.0000.7070.7070.7070.7070.7070.7071.0000.6170.6170.6170.6220.6170.6170.6170.1010.0570.0230.0000.0000.0330.0000.2490.2470.2470.2470.247
nr_010.0470.0790.0000.0000.1220.0000.2190.4390.0000.0660.0000.2550.3560.0000.0290.0360.0370.0370.2930.1620.0410.2290.0000.0000.1930.1870.0000.0000.0270.0000.0200.0000.1860.1920.0450.0000.1740.1860.0000.0680.1060.0000.0160.0000.0280.1430.1830.1920.0580.3230.1980.0000.2150.2360.0000.0530.0350.0610.3670.1270.1890.1890.1880.0000.0000.0000.0000.0320.0000.0000.0920.1200.0000.0000.0620.0000.0460.0000.1310.0000.0000.0000.0270.0390.0000.0000.0000.0060.0000.0000.0000.0000.6170.6170.6170.6170.6170.6170.6171.0000.7070.7070.7070.7070.7070.7070.0600.0640.0470.0430.0360.0550.0370.2300.2280.2280.2280.228
nr_020.0460.0770.0000.0000.1280.0000.2390.2680.0000.0230.0050.2410.3550.0220.0430.0620.0650.0000.2930.1600.0420.2340.0000.0000.1930.1720.0000.0000.0380.0000.0080.0000.1790.1900.0430.0000.1750.1880.0000.0110.0470.0000.0140.0000.0250.1440.1780.1920.0000.3240.1940.0000.2290.2320.0000.0470.0000.0240.3390.1140.1790.1890.1880.0000.0000.0000.0710.0320.0000.0000.0910.1280.0000.0480.0940.0000.0470.0000.0000.0000.0160.0100.0000.0030.0000.0000.0090.0280.0030.0000.0000.0000.6270.6170.6180.6170.6170.6170.6170.7071.0000.7070.7070.7070.7070.7070.0620.0370.0080.0000.0000.0000.0000.2290.2280.2280.2280.229
nr_030.0460.0770.0000.0000.1170.0000.2290.2630.0000.1730.0230.2470.3550.0000.0490.0480.0520.0000.2930.1690.1260.2310.0000.0000.1930.1790.0000.0000.1280.0000.0060.0000.1770.1940.0630.0000.1780.1860.0000.0140.0560.0000.0040.0000.0540.1490.1760.1910.0350.3230.1950.0000.2300.2460.0000.0210.0000.0300.3350.1170.1790.1930.1880.0000.0000.0140.0000.0440.0000.0000.0940.1210.0000.0000.0600.0000.0440.0290.0000.0160.0180.0000.0000.0200.0000.0000.0110.1440.0150.0000.0000.0000.6170.6170.6170.6170.6180.6170.6170.7070.7071.0000.7070.7070.7070.7070.0680.0750.1090.0190.0000.0210.0000.2290.2280.2280.2280.228
nr_040.0520.0850.0000.0230.1220.0150.2580.2680.0220.0330.0090.2450.3570.0000.0410.0370.0560.0000.2970.1730.0430.2300.0000.0000.1950.2110.0000.0000.3250.0000.0090.0000.1780.1890.0460.0000.1750.1870.0000.0520.0460.0000.0410.0000.0160.1440.1760.1900.0310.3240.2010.0000.2330.2420.0000.0250.0000.0890.3680.1230.1790.1890.1880.0000.0000.0000.0000.0330.0000.0000.1070.1230.0000.0000.0570.0000.0560.0000.0000.0000.0000.0280.0000.0000.0000.0110.0000.0740.4600.0000.0000.0000.6170.6170.6180.6170.6170.6170.6220.7070.7070.7071.0000.7070.7070.7070.0750.1330.3420.0000.0000.0340.0000.2290.2280.2300.2280.229
nr_070.0470.0770.0000.0430.1120.0000.2240.2580.0000.0740.0000.2390.3550.0000.0300.0370.0360.0000.2940.1600.0410.2400.0000.0000.1930.1680.0000.0000.0250.0000.0230.0000.1750.1880.0430.0000.1720.1850.0000.0130.0450.0000.0090.0000.0130.1420.1750.1890.0000.3230.1900.0000.2870.2300.0000.0210.0000.0460.3300.1360.1790.1880.1880.0000.0000.0000.0000.0310.0000.0000.1160.1390.0000.0000.0570.0000.0380.0610.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.6170.6170.6520.6170.6170.6170.6170.7070.7070.7070.7071.0000.7070.7070.1000.0410.0000.0000.0000.0240.0000.2290.2280.2280.2280.228
nr_080.0470.0790.0000.0000.1130.0000.2420.2710.0000.0220.0880.2410.3550.0000.0290.0310.0370.0370.2970.1570.0410.2320.0000.0000.1990.1700.0000.0000.0270.0000.0120.0000.1800.1940.0450.0000.1740.1910.0000.0190.0450.0000.0280.0000.0190.1430.1790.1940.0000.3230.1980.0000.2440.2590.0000.0250.0000.0290.3390.1130.1790.1950.1880.0000.0000.0000.0000.0320.0000.0000.0910.1220.0000.0000.0570.0000.0390.0000.0000.0000.0000.0000.0090.0290.0000.0000.0000.0000.0000.0000.0000.0000.6170.6490.6170.6170.6170.6170.6170.7070.7070.7070.7070.7071.0000.7070.0750.0360.0000.0000.0000.0110.0000.2290.2290.2280.2560.228
nr_110.0510.0780.0000.0000.1170.0310.2600.2610.0000.1120.0120.2490.3560.0000.0340.0360.0380.0000.2980.1740.0410.2300.0000.0000.1940.1730.0000.0000.0720.0000.0170.0000.1800.1910.0490.0000.1750.1880.0000.0170.0460.0420.0370.0000.0280.1450.1770.1920.0000.3240.2030.1050.2700.2360.0000.0450.0000.0230.3320.1250.1790.1890.1880.0000.0310.0000.0000.0440.0000.0000.0900.1190.0000.0340.0570.0000.0400.0000.0000.0000.0540.0000.0000.0000.0000.0000.0110.0670.0000.1000.0000.0000.6170.6170.6170.6170.6170.6170.6170.7070.7070.7070.7070.7070.7071.0000.0650.0450.0400.0000.0000.0000.0000.2290.2280.2310.2280.228
post_im0.0410.0330.0240.0710.0200.0000.0360.0630.0000.0170.0210.0270.0370.0600.0280.0580.0800.0260.0650.1380.0000.0770.0620.0000.1310.0980.0310.0380.0680.0000.0840.0500.1260.1310.0730.0250.1050.1230.0590.0200.0640.0380.0160.0000.0000.0270.1050.1130.0310.0700.0360.0000.0570.1060.0000.0470.0620.0000.0900.4350.0280.0000.0000.0440.0460.0860.0600.0510.0540.0410.4570.3980.0310.0480.0110.0540.0380.0410.0490.0160.0250.1140.0370.0630.0240.0300.0170.0360.0180.0920.0000.0810.0420.0470.0450.0410.0450.0420.1010.0600.0620.0680.0750.1000.0750.0651.0000.0590.0000.0000.0000.0760.0540.0000.0000.0000.0320.000
ritm_ecg_p_010.0040.0290.0000.0170.0860.0000.0470.1400.0000.0400.0310.0920.0660.0030.0330.0350.0080.0350.0740.1940.0370.0940.0000.0410.1150.1600.0190.0000.2250.0850.0170.0000.1010.1060.0890.0000.0930.1060.0000.1160.0980.0000.0550.0580.0360.0320.1010.1140.1030.0420.0650.0000.0690.1270.0000.0280.0280.0530.1470.1210.0680.0450.0290.0000.0000.0000.0000.0000.0000.0000.0820.0860.5790.5780.5790.5780.5900.5800.5790.5780.5840.5780.5790.5780.5780.5790.5780.6050.5960.5790.5780.5800.0360.0360.0470.0650.0380.0360.0570.0640.0370.0750.1330.0410.0360.0450.0591.0000.7510.7170.7070.8900.7280.0000.0340.0290.0000.030
ritm_ecg_p_020.0000.0270.0000.0000.0230.0000.0310.1070.0000.1030.0000.0710.0000.0000.0000.0000.0170.0000.0220.1590.0730.0690.0000.0760.0470.1430.0000.0000.5710.0810.0060.0000.0380.0690.0370.0000.0390.0690.0000.0740.0000.0000.0570.0520.0460.0000.0490.0690.0650.0000.0370.0110.0500.0720.0000.0000.0230.0910.1170.0970.0180.0140.0000.0000.0000.0310.0000.0000.0000.0000.0810.0590.5780.5790.5780.5780.5800.5790.5780.5780.5810.5780.5790.5790.5790.5810.5800.7310.6970.5790.5780.5820.0000.0000.0480.0470.0000.0000.0230.0470.0080.1090.3420.0000.0000.0400.0000.7511.0000.7070.7070.7130.7070.0080.0000.0560.0080.000
ritm_ecg_p_040.0570.0440.1440.0000.0000.0000.1370.1450.0400.0000.0230.0830.0420.0000.0000.0000.0350.0000.1270.1530.0650.1010.0750.0270.1010.1040.0130.0000.0350.0460.0140.0000.0910.0730.0000.0000.0780.0800.0000.0340.0430.0250.0480.0780.0290.0000.0950.0950.0270.0420.0610.0590.0000.1790.0000.0380.0250.0240.0230.1120.0170.0000.0000.0000.0240.0000.0000.0000.0000.0000.1030.0650.5780.5780.5780.5780.7510.5790.5780.5780.5780.5780.5780.5790.5790.5790.5790.5790.5790.5830.5780.5800.0000.0000.0000.0510.0000.0000.0000.0430.0000.0190.0000.0000.0000.0000.0000.7170.7071.0000.7070.7080.7070.0260.0370.0240.0000.000
ritm_ecg_p_060.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.1390.0950.0680.0000.0270.0000.0380.0000.0000.0000.0000.0220.0000.0000.0240.0370.0000.0000.0370.0000.0340.0000.0000.0460.0450.0240.0000.0000.0410.0150.0000.0000.0000.0000.0000.0000.0380.0220.0300.0610.0920.0450.1040.0000.0000.0000.0000.0000.0000.0000.0000.0670.0430.5780.5780.5780.5780.5940.5790.5780.5780.5780.5780.5780.5780.5780.5800.5780.5780.5780.5790.5780.5800.0000.0000.0000.0440.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.7070.7070.7071.0000.7070.7070.0000.0000.0000.0000.000
ritm_ecg_p_070.0210.0000.0200.0000.0580.0000.0220.0970.0000.0000.0310.0510.0470.0000.0310.0330.0000.0090.0570.1650.0000.0880.0000.0320.0760.1240.0230.0080.0510.0770.0180.0000.0740.0820.0790.0000.0770.0830.0000.1090.1150.0000.0450.0460.0660.0390.0740.0920.0970.0080.0000.0000.0180.0690.0000.0490.0240.0000.0960.1480.0690.0130.0250.0000.0000.0000.0000.0000.0040.0000.0980.1200.5800.5790.5790.5780.5800.5820.5790.5780.5800.5780.5780.5780.5790.5790.5790.5800.5810.5790.5780.5800.0150.0070.0160.0540.0060.0150.0330.0550.0000.0210.0340.0240.0110.0000.0760.8900.7130.7080.7071.0000.7100.0070.0000.0000.0000.028
ritm_ecg_p_080.0000.0050.0000.0000.0220.0000.0000.0620.0180.0270.0000.0000.0000.0000.0000.0000.0290.0000.0100.1340.0000.0830.0000.0280.0000.0490.0000.0000.0280.0730.0040.0000.0280.0380.0000.0000.0240.0490.0000.0510.0130.0000.0510.0460.0220.0000.0430.0500.0170.0220.0000.0000.0420.0370.0000.0230.0260.0000.0000.1060.0360.0000.0200.0000.0000.0350.0400.0000.0280.0000.0850.0660.5780.5780.5780.5780.5780.5800.5780.5780.5790.5780.5780.5780.5780.5790.5780.5780.5780.5790.5780.5800.0000.0000.0000.0450.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0540.7280.7070.7070.7070.7101.0000.0370.0090.0230.0370.016
zab_leg_010.0170.0360.0000.0190.0000.0000.1490.0840.0000.0390.0260.1150.2620.0000.0000.0000.0190.0000.0660.1540.0000.2650.0000.0340.0710.1510.0040.0170.0190.0000.0000.0000.0520.0500.0000.0000.0650.0540.0000.0000.0300.0000.0000.0000.0500.0700.0520.0460.0000.2800.0900.0220.1530.1570.0250.0000.0400.1090.1370.0000.4020.4200.4200.0000.0000.0000.0000.0000.0000.0000.0430.0210.0000.0000.0000.0000.0480.0100.0000.0130.0000.0000.0000.0390.0090.0640.0260.0260.0000.0000.0000.0000.2480.2480.2470.2480.2480.2480.2490.2300.2290.2290.2290.2290.2290.2290.0000.0000.0080.0260.0000.0070.0371.0000.7090.7070.7070.707
zab_leg_020.0360.0120.0380.0000.0580.0000.1520.1390.0730.0450.0000.1420.2640.0000.0000.0090.0000.0000.0860.1900.0000.2650.0980.0390.0800.1780.0000.0250.0290.0000.0170.0310.0790.0820.0400.0000.0660.0500.0120.0790.0250.0000.0220.0250.0710.0750.0690.0710.0000.2800.1360.0000.1540.1530.0000.0040.0000.0000.1460.0110.4010.4200.4200.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0350.0680.0000.0000.0000.0000.0000.0000.0000.0270.0000.0450.0360.0000.0000.0000.0000.2470.2470.2470.2470.2470.2470.2470.2280.2280.2280.2280.2280.2290.2280.0000.0340.0000.0370.0000.0000.0090.7091.0000.7070.7070.707
zab_leg_030.0000.0000.0000.0000.0000.0070.1570.1490.0000.0940.0000.1090.2650.0000.0130.0050.0100.0000.0620.0950.0000.2640.0000.0000.0650.1660.0000.0000.0680.0000.0000.0000.0570.0470.0000.0000.0540.0450.0000.0000.0280.0000.0370.0270.0000.0690.0630.0460.0000.2820.0820.0100.1560.1280.0000.0290.0050.0000.1570.0350.4010.4220.4210.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0350.0180.0000.0000.0000.0000.0000.0000.0000.0000.0030.0760.0000.0430.0000.0000.2470.2470.2470.2470.2470.2470.2470.2280.2280.2280.2300.2280.2280.2310.0000.0290.0560.0240.0000.0000.0230.7070.7071.0000.7070.707
zab_leg_040.0000.0220.0000.0000.0000.0000.1450.0820.0000.0000.0550.1140.2620.0280.0000.0000.0540.0000.0660.0760.0000.2630.0000.0000.0900.1480.0000.0000.0510.2100.0000.0230.0600.0750.0000.0000.0630.0530.0000.0000.0570.0000.0000.0000.0000.0670.0580.0410.0300.2810.0860.0540.1570.1250.0000.0000.0000.0000.1340.0000.4000.4200.4200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0000.0340.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2470.2470.2470.2470.2470.2470.2470.2280.2280.2280.2280.2280.2560.2280.0320.0000.0080.0000.0000.0000.0370.7070.7070.7071.0000.707
zab_leg_060.0000.0000.0000.0000.0000.0000.1330.0730.0000.0000.0170.1110.2620.0000.0000.0000.0160.0000.0680.0640.0000.2650.0000.0000.0720.1460.0000.0000.0150.0000.0000.0000.0550.0480.0000.0000.0570.0490.0000.0000.0280.0000.0000.0000.0000.0740.0550.0440.0000.2800.0840.0160.1330.1140.0000.0300.0000.0000.1360.0750.4000.4200.4200.0000.0000.0000.0000.0000.0000.0000.0210.0280.0000.0000.0000.0000.0350.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2470.2470.2470.2470.2470.2470.2470.2280.2290.2280.2290.2280.2280.2280.0000.0300.0000.0000.0000.0280.0160.7070.7070.7070.7071.000

Missing values

2024-11-19T17:24:55.092937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-19T17:24:56.782432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDAGESEXINF_ANAMSTENOK_ANFK_STENOKIBS_POSTIBS_NASLGBSIM_GIPERTDLIT_AGZSN_Anr_11nr_01nr_02nr_03nr_04nr_07nr_08np_01np_04np_05np_07np_08np_09np_10endocr_01endocr_02endocr_03zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06S_AD_KBRIGD_AD_KBRIGS_AD_ORITD_AD_ORITO_L_POSTK_SH_POSTMP_TP_POSTSVT_POSTGT_POSTFIB_G_POSTant_imlat_iminf_impost_imIM_PG_Pritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10n_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08GIPO_KK_BLOODGIPER_NANA_BLOODALT_BLOODAST_BLOODKFK_BLOODL_BLOODROETIME_B_SR_AB_1_nR_AB_2_nR_AB_3_nNA_KBNOT_NA_KBLID_KBNITR_SNA_R_1_nNA_R_2_nNA_R_3_nNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nLID_S_nB_BLOK_S_nANT_CA_S_nGEPAR_S_nASP_S_nTIKL_S_nTRENT_S_nFIBR_PREDSPREDS_TAHJELUD_TAHFIBR_JELUDA_V_BLOKOTEK_LANCRAZRIVDRESSLERZSNREC_IMP_IM_STENLET_IS
017712112?30700000000000000000000000??1801000000001000000001000001000000000010000000000004.70138???8164001???00000001001100000000000000
125511000000000000000000000000000000??120900000004100010000000010000000000000000000000013.501320.380.18?7.83200010100001001011101000000000000
235210002?20200000000000000000000000150100180100000000410001000000010000000000000000000000000401320.30.11?10.8?330011101003221101100000000000000
346800002?20310000000000000000010000??120700000000110010000000000000000000000000000000013.901460.750.37???2001???00000000011100000000001000
456010002?30700000000000000000000000190100160900000004100000001000000000000000000000000000013.501320.450.22?8.3?900000000000000010101000000000000
566410121?00000000000000000000000000??1409000000011000000010000000000000000000000000000????0.450.22?7.22200001000000000101100100000000000
677011121?20710000000000000000010000120801208000000000300100000000000000000000000000000000????0.30.11?11.15100001000000000001101000000001000
786510112?20700000000000000000000000??145950000000020010000000000000000000000000000000004.50136???6.220730000000000000011100000000000000
896010002?2060000000000000000000000020012019512000000000320100000100000000000000000000000000????0.30.37?6.2330000100100000100????100000000000
9107702000?30610000000000000010000000??20010000000041000000010001000000000000000000000000????0.380.11?6.9303000???00001000010000000000001000
IDAGESEXINF_ANAMSTENOK_ANFK_STENOKIBS_POSTIBS_NASLGBSIM_GIPERTDLIT_AGZSN_Anr_11nr_01nr_02nr_03nr_04nr_07nr_08np_01np_04np_05np_07np_08np_09np_10endocr_01endocr_02endocr_03zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06S_AD_KBRIGD_AD_KBRIGS_AD_ORITD_AD_ORITO_L_POSTK_SH_POSTMP_TP_POSTSVT_POSTGT_POSTFIB_G_POSTant_imlat_iminf_impost_imIM_PG_Pritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10n_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08GIPO_KK_BLOODGIPER_NANA_BLOODALT_BLOODAST_BLOODKFK_BLOODL_BLOODROETIME_B_SR_AB_1_nR_AB_2_nR_AB_3_nNA_KBNOT_NA_KBLID_KBNITR_SNA_R_1_nNA_R_2_nNA_R_3_nNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nLID_S_nB_BLOK_S_nANT_CA_S_nGEPAR_S_nASP_S_nTIKL_S_nTRENT_S_nFIBR_PREDSPREDS_TAHJELUD_TAHFIBR_JELUDA_V_BLOKOTEK_LANCRAZRIVDRESSLERZSNREC_IMP_IM_STENLET_IS
169016917710000?20?00000000000000000000000??1408000000000400100000100000000000000000010000000???????5.41350001??00000000010000000010100003
169116926210000?01700000000000000001000000??140800000004100010000000000000000000000000001000004.901330.150.07?10.5310?????01??2??1011100000000100003
169216937100622?20700000000000000000000000??110700000004200010000000000000000000000000000000013.401330.520.22?5.181100???01001000111100100000100103
169316947000221?20700000000000000000000000??1408000000041000100000001000000000000000000000000????0.520.48???10??11100??1??0001000000000100003
169416957700000?20700000000000000000000000??150900000004100010000000000000000000000000000000013.901360.230.18?5.520300010010000100011000000000100003
169516967700421?20700000000000000000000000??11070000000??00010000000000000000000000001000000013.701301.050.52?12.8620??11100??0??0000000000010100003
169616977000621?20700000000000000000001000??50001000000210100000000000000000000000000000000?????????20??1??00??0??1000000000000000001
169716985513622?00000000000000000000000000??70500?000041000??????????????????????????0000000????0.230.15?8.3131200???12000000101100000000000106
169816997902221?207?0000100000000000000000??11070001000????001000000001000000000000000000000013.101360.450.45?7.5421??10112??1??1011100000001000001
169917006312????20?40000001000000000001000??0001000100200000010000000000000000000000000000?????????10?????10??0??0000000000000000001